Posts in category: Microsoft AI

Transcript 

RAFAH HOSN: What has changed is that in the old days, we had the luxury of creating something, going and piloting for three months until we know whether it works or not, and then taking one year to productize! That … that, that doesn’t work anymore! Because guess what? In three months, this innovation is, like, topped by four other innovations, be it at Microsoft or elsewhere. So that speed is really shifting the mindset and the spirit of people. 

GRETCHEN HUIZINGA: You’re listening to Ideas, a Microsoft Research Podcast that dives deep into the world of technology research and the profound questions behind the code. I’m Dr. Gretchen Huizinga. In this series, we’ll explore the technologies that are shaping our future and the big ideas that propel them forward. 

My guest today is Rafah Hosn. She’s a partner, group product manager for AI Frontiers at Microsoft Research. I’d call Rafah a sort of organizational conductor, working both with leaders to drive clarity around the mission as well as program managers to make sure they have solid operational strategies to execute on it. Rafah has mad skills in bringing research ideas from lab to life, and I’m thrilled to talk to her today. Rafah Hosn, welcome to Ideas! 

RAFAH HOSN: Thank you, Gretchen. Oh, my goodness, I have to live up to this introduction now! [LAUGHTER] 

HUIZINGA: Well, before we talk about research ideas, let’s talk about you and your own sort of “reason for being” in the research world. How would you describe your motivation for working in research and—assuming there was one—what was the “big idea” or animating “what if?” behind what you’re doing today? 

HOSN: Yeah, you know, I don’t know. There are so many big ideas, to be honest! Every day, I wake up and I often tell my husband how lucky, like so totally lucky and humbled, I am to be where I am right now in this moment, like right now when society as we know it is being totally disrupted by this huge leap in AI. And why research? Well, I’ve tried it all, Gretchen! I’ve been in research, I went to product, I did engineering, and I did full circle and came back to research. Because, you know, for me personally, there’s no other environment that I know of, for me, that has this amount of creativity and just infinite curiosity and intellect. So working with people that are asking “what next?” and trying to imagine the next world beyond where AI is today is just … this is the big idea. This is why I’m here. This is why I’m excited to come to work every day. 

HUIZINGA: Yeah. Well … and I want to drill in a little bit just, sort of, personally because sometimes there’s a story, an origin story, if you will, of some pivotal aha moment that you say, oh, that’s fascinating, that’s cool, that’s what I want to do. Anything that piqued your interest way back when you were a kid or, sort of, a pivotal moment in your educational years? 

HOSN: Yeah, you know, so many different things that inspire you along the journey, right. It’s not just one thing, Gretchen. My dad was a doctor. He was my biggest inspiration growing up. And the reason is because he had a lot of depth of knowledge in his domain. And I wanted that. I wanted to have depth of knowledge in a domain. So I went engineering against his advice. He really wanted me to be a doctor. [LAUGHTER] So he was not too happy. But, you know, throughout my education, you know, I was there when smartphones came about, when the internet was a thing. And now, like with generative AI, I feel like I’ve lived through so many disruptions, and every one of those was, “Oh my gosh! Like, I am exactly where I want to be!” So multiple inspirations, and every day, I wake up and there’s new news and I’m saying to myself, “OK, that’s great.” I love it! 

HUIZINGA: What a time to be alive! 

HOSN: It is amazing!

HUIZINGA: Yeah. Well, you recently took on this new role in AI Frontiers at Microsoft Research. And that very word “frontiers” evokes images of unexplored, uncharted territories like the Wild West or for Trekkies, maybe “space: the final frontier.” So what does it mean to you to be working at the frontier of artificial intelligence, and what’s the big idea behind AI Frontiers? 

HOSN: You know, it’s my biggest and most exciting adventure so far! Working under Ece Kamar’s leadership in this AI Frontiers is really trying to push ourselves to think, what’s beyond what there is right now in artificial intelligence? Where can we push more, from a scientific perspective? How do we translate these scientific discoveries into capabilities that people can actually use and derive value from? It’s a big responsibility, as well, because we just don’t want to push the boundaries of AI for the sake of pushing. We want to push it in a safe and responsible way. So it is a big responsibility. 

HUIZINGA: Yeah … 

HOSN: And fundamentally, you know, the unifying big idea in this team is to explore, you know, how far can we push intelligence further into models and encapsulations of those models so that we can, you know, have not just sort of an assistant but really a personal assistant, an agent that can, kind of, do tasks for us, with us, seamlessly across multiple domains? So this is what we’re trying to push for. 

HUIZINGA: Mmm. Rafah, do you feel like you’re at the frontier of artificial intelligence? I mean, what are the emotions that crop up when you are dealing with these things—that you and your teams basically know about but the rest of us don’t?

HOSN: For most days, it’s excitement. Sometimes it’s [LAUGHTER] … it ranges, to be honest. I would say there’s a spectrum of emotions. The dominating one is really just excitement. There’s so much that has happened with GenAI, but I feel like it has opened up so many different paths, as well, for us to explore, and that’s the excitement. And then every time the world accomplishes something, you’re like in astonishment. You’re like, wow, wow. 

HUIZINGA: Yeah … 

HOSN: And then, and then, oh my gosh, what’s next? And so, it’s a range of emotions … 

HUIZINGA: Right … 

HOSN: … but I would say the dominating one is enthusiasm.

HUIZINGA: Yeah. Well, I’ve heard other people on your teams use words like surprise, sometimes even shock … 

HOSN: Yeah, yeah, there are a lot of “wow” factors. Every day, every day, I wake up, I read like my three favorite AI tweets or things like that, and I’m like, “Oh my gosh. I wouldn’t have imagined that this model could do this thing,” so [LAUGHS] … um, but it’s exciting. 

HUIZINGA: We may have to get those accounts in the show notes so that we can follow along with your surprise and amazement in the mornings! 

HOSN: [LAUGHS] Yes! 

HUIZINGA: Well, listen, when we talk about measuring the success of an AI system, we often use the common convention of what we call benchmarks. But I want to zoom out from AI systems for a minute and ask how you might measure the success of an AI lab, which is what you’re working in. What are your benchmarks or key performance indicators—we call them KPIs—for the work going on at AI Frontiers? 

HOSN: Yeah, so I’m going to start by something that may sound surprising maybe to some, but I think it’s the culture first. It’s the culture of endless curiosity, of enthusiasm coupled with a bit of skepticism, to be honest, to ask the questions, the right questions, and this drive to push further. So I would say one KPI of success for me, personally, is, you know, can we maintain this culture of enthusiasm coupled with skepticism so we can ask hard questions and an envelope of enthusiasm and drive for everyone? So that’s one. I would say the other three are … one is around how much can we push scientifically as a community, right? This is a team of people that are getting together with a mission to push the boundaries of our understanding of artificial intelligence. So are we pushing that scientific boundaries? Are we creating insights, not just for the scientific community, but also for Microsoft and the world, so that we know how to derive value from these discoveries, right? At the end of the day, it is awesome to push scientifically. It’s even more awesome if you take this and translate it into something a human being can use … 

HUIZINGA: Yeah … 

HOSN: … or an enterprise can use. And I think … that’s kind of my KPIs of success. Culture first, pushing on the scientific boundaries, creating insights for the scientific community as well as for Microsoft so we can derive value for us as a society, right. 

HUIZINGA: Yeah. Well, continuing on this idea of success, and you’ve alluded to this already in terms of characteristics of curiosity and so on, part of your job, as you put it, was “enabling brilliant minds to find success.” So talk a little bit about the personal qualities of these brilliant minds and how you help them find success.

HOSN: Yeah, you know, everybody I work with brings different aspects of brilliance to the table—every day. So in our community of engineers, PMs, researchers, everybody is present with their ideas and their strengths. And they’re pulling together to push harder and faster on our key priorities. And I find folks working in AI these days, you know, to have a renewed fire. It’s really amazing to see. And I talk a lot about curiosity, but, you know, I cannot emphasize how much this is driving a lot of our community to explore new paths that they hadn’t thought about prior to this GenAI coming along. And so everybody is showing up, present, asking these questions and trying to solve new scenarios, new problems that are emerging. And from my perspective, you know, as you mentioned, I just try to unblock, basically. My team and I are here to [LAUGHTER] … well, two things I would say. First is bring the outside-in perspective. That’s so important because science is amazing, but unless you can derive value from it, it remains an awesome paper and an awesome equation, right. So asking, who can use this? What are the scenarios it could, you know, light up? How can we derive value? So those are the questions that my team and I can contribute to, and we are trying to participate from ideation all the way to basically delivering on key milestones. And that last mile is so important. Like, once you know what you want to do, how do you structure? How do you have an operational strategy that is amenable to these times, which is fast, fast, fast, and faster? So that’s, kind of, what we’re trying to do here. 

HUIZINGA: Yeah, yeah. Well, two things came to my mind in terms of what kinds of people would end up working in this area. And one would be agility, or agile. And that would, to me, represent in a researcher that the person would be able to spin or pivot if something didn’t work out. And the other one is sort of a risk-reward mentality. It’s like, where are you willing to push to get that reward versus what might keep you from even trying? 

HOSN: Yeah, so definitely in this AI Frontiers community, I’m finding a lot of adaptability. So people willing to try, failing fast when they fail, and pivoting. And you have to, nowadays, in this atmosphere that we are living in. And because we have the privilege of working in research—and it’s really an honor and a privilege, and I’m not saying it just lightly—but it is the place where you can take risks, Gretchen. It is the place where failing is totally fine because you’re learning and you’re pivoting in a way that allows you to progress on the next thing you tackle. So I feel like most of the people I work with in this community, AI Frontiers, we are risk takers. We want to push, and it’s OK to fail, and it’s OK to adapt. So, I think, as an aggregate, that’s kind of the spirit I’m seeing. 

HUIZINGA: In the past, Rafah, you’ve stressed the importance of both teams and timing. And so we’ve been talking about the teams and the minds and the kinds of qualities in those people. But what about the “when” of research? How does timing impact what gets done in your world?

HOSN: Well, in this new era, Gretchen, everything is yesterday! [LAUGHS] I mean, it is true. AI research is moving at such speeds that I feel like we need to get accustomed to a timing of now. And if it’s not now, it’s yesterday. So the timing is important, but the leeway has shrunk so significantly that I feel like we have to really just be present in the moment and just move as fast as we can because everybody else is moving at the highest speed. So timing is “now,” is what I would say. 

HUIZINGA: On that note, with so many innovations in AI coming out every day, every minute, what you’ve just expressed is that research horizons are shorter than ever. But as one of your team members noted in a recent panel, it still takes a lot of time to translate a research artifact, maybe a noteworthy finding or a published paper or an equation, an algorithm, into a useful product for humans. So how are you then dealing with these newly compressed timelines of “it needs to be done yesterday to keep up,” and how has the traditional research-to-product pipeline changed? 

HOSN: Yeah, it’s an awesome question. It is so true that going from research to a production-quality algorithm or capability takes time. But what I’m seeing is that the research-to-capabilities is accelerating, meaning if you look at the world today in generative AI and its surrounding, folks even in research are creating assets as they are creating their research. And so they are thinking as well, how do I showcase this? And of course, these assets are not production ready. But here’s the kicker. I think that the product teams are also adapting to this generative AI era, and they are changing to meet this disruptive moment. They are changing the way they think, and they are accelerating the way they productize and look at hardening and securing the assets so that they can put them in the hands of even a limited set of users just to get a feel of what it means to have them in the hands of end users and quickly iterating so that they can further harden and further improve the design until it’s production ready. And I feel like our product partners are meeting the moments, meaning they also are really adapting their processes such that they can get these assets and put them in the hands of users and test them out before they actually release them. 

HUIZINGA: Right. Let’s drill in a little bit more on that and talk about the traditional research-to-product pipeline, where you would have a researcher working on something and then an RSDE. What does RSDE stand for? 

HOSN: A research software development engineer. It’s a mouthful. 

HUIZINGA: Right. And then to the PM, or program manager, and then to the engineer. And you’ve said this provocative statement: now everyone is a PM! 

HOSN: Everyone is a PM! [LAUGHTER] 

HUIZINGA: What do you mean by that?

HOSN: I just, I just feel like if we are to meet the moment, we need to be thinking outside-in, inside-out simultaneously. And I believe that the spirit of program management, which is looking at the design from a user-centric perspective, is embedded as we are ideating, as we are trying to explore new methodologies, new algorithms, new assets. And so what has changed is that in the old days, we had the luxury of creating something, going and piloting for three months until we know whether it works or not, and then taking one year to productize! That … that, that doesn’t work anymore. [LAUGHTER] 

HUIZINGA: Right. 

HOSN: Because guess what? In three months, this innovation is, like, topped by four other innovations, be it at Microsoft or elsewhere. So that speed is really shifting the mindset and the, and the spirit of people. I have colleagues and friends, researchers, that are asking me, oh, scenarios, users … I mean it’s amazing to see. So, yes, everybody has gotten a little PM in them now. [LAUGHTER] 

HUIZINGA: Yeah, I did a podcast with Shamsi Iqbal and Jina Suh. And Shamsi was talking about this concept, this old concept, of the researcher being in their lab and saying, well, I’ve done this work; now go see what you want to do with it. I don’t think you have that affordance anymore as a researcher. 

HOSN: No … 

HUIZINGA: You’ve got to work much more tightly with other team members and think like a PM. 

HOSN: Totally. 

HUIZINGA: So let’s talk about how the big general idea behind AI Frontiers is giving birth to smaller, more specific ideas. What are some of the research directions and projects that you could tell us about that illustrate this vision here? 

HOSN: Yeah, and I’m sure you’ve heard some of it come from Ece Kamar as she spoke on this community that we have. In AI Frontiers, we’re exploring, I would say, three major areas of research. And I want you to imagine a stack. At the bottom of the stack, we’re asking ourselves questions around, what are some new architectures we can be thinking about for these foundational models? How do we create them? What kind of data we need to train them, to pre-train them. And then on top of that stack, which starts with a foundation model, we’re asking ourselves, OK great, you have a pretrained model. In a lot of cases, when you’re creating especially small models, you need to fine-tune them. So what is this methodology and data generation pipeline that we’re going to use to fine-tune these models and specialize them for both across domains and across skill set? And on top of that—so now we’re on the third layer—we have a final layer that encapsulates these models and orchestrates among them to allow them the ability to do, you know, complex tasks. And we don’t want to stop there because for us it’s … we don’t want to have an agent that just does things and doesn’t learn. So that learnability, that learning on the job, like we do as humans, is something we’re asking ourselves, as well. Like, how do we encapsulate these models? We orchestrate among them. And we allow these encapsulated things, we call them agents, to learn on the job so that they can accomplish more complex tasks. So those are the three things. And then cutting across these three layers, imagine there’s a thing that cuts across them, is doing everything in a way that allows us to rigorously evaluate and to ensure that we’re doing things in a safe and responsible way. So those are the main things that we’re working on. Does that make sense? 

HUIZINGA: That’s … yes, it does. And I imagine, you know, if you go to the website and you see those, kind of, three main areas, I imagine that even under there, there are specific projects on, you know, how then do we iterate? How then do we explore? 

HOSN: That’s right. That’s a good plug for people to visit the AI Frontiers website! Thank you, Gretchen! [LAUGHS] 

HUIZINGA: Well, I’ve been intrigued for a while by this idea of what you’ve called bi-directional enrichment, which represents both how research informs product but also how product informs research, but you’ve recently talked about how this idea has expanded to embrace what you call multi-directional enrichment and co-innovation. So what do you mean by that, and what does it look like for you? 

HOSN: So we talked just moments ago how the time has shrunk tremendously in artificial intelligence and the speed at which innovations are coming out. So what does that mean when you are sitting in research and you’re trying to derive value for Microsoft, for example? It means that now, rather than going on a journey to try out you know different things, what you want is for product to come on a co-innovation journey with you. And not every team has the capability or the time or the resources to do it. But sometimes product teams have applied scientists that are asking themselves very similar questions. And so now we have this huge synergistic effect by which, you know, researchers can come and explore their research but anchor them in a real-world scenario that the product team is, you know, asking themselves about. And that’s what I mean by co-innovation. And we look for co-innovation, so these are product teams or applied scientists in product teams that are not looking at something I can ship tomorrow. Because that’s not … that’s not frontiers. That’s feature-function that they can deliver right now to their customers. When we co-innovate, we have to co-innovate on a bit of a longer timespan. Now it’s no longer years, right? With generative AI, everything is months, but nonetheless, this is not next week. This is in a few months. And so … but this is really, really great because, again, I keep saying this and I have maybe a huge bias, but I do believe that research, without it being anchored in real-world scenario, just doesn’t have the same effect. So I have a bias for that. It’s my PM hat, what can I say? I love real-world scenarios! [LAUGHTER] 

HUIZINGA: What you just referred to is an interesting flow. I’ve noticed in my years doing this podcast that some people that started in research ended up over in product—and we’ll call them embedded researchers, if you will—and then some people that were in a product scenario come back over to research. And so, there’s this flow, multi-directional, bi-directional, and also where they’re placed within the company. How do you see that flow and the value of that flow between these organizations? 

HOSN: Yeah, you know, like, I think that the flow is important because that’s how cross-pollination happens. And you talked about brilliant minds. In product teams, there are brilliant minds, as well, right. And although their focus area is more around the product they live and breathe every day, this is enriching to researchers and continues to be enriching because when you deploy research capabilities in a real-world setting, there are surprising new research questions that come up, not just engineering. A lot of times people think of research, OK, yeah, you scale it, you harden it, you secure it, and it’s good to go. But that’s not always the case. In a lot of cases, because of the interactivity that happens with real-world scenarios, it opened up brand-new paths for research. And so I think that flow continues to happen even now. It’s just compressed. It’s just that researchers are no longer thinking six years. Researchers are thinking three months. Like, what am I going to do in three months? Because in three months, there will be a hundred other researchers that are coming up with innovation on the same question. So I think the flow still exists. I think that time has shrunk. And I think the mobility from researchers and research going to product and vice versa is enriching for the people that do it because you gain different perspectives. 

HUIZINGA: Well, and let’s push in even there a little bit. Researchers like everyone else can get comfortable looking at things through a particular lens. I would say that’s a human trait, not just a research trait … 

HOSN: Absolutely. 

HUIZINGA: … until a disruption challenges their status quo. So you’ve talked about LLMs, which we’ve called large language models, as being a good forcing function for researchers to think differently, even about the questions they’re asking. Can you elaborate on that a little bit? 

HOSN: Yeah, yeah, so, you know, the large language models and this disruption that we are living in at the moment is lighting fire underneath a lot of people’s intellect, I’m going to say. And so I think that people have to adapt quickly to change. And this is key. Adaptability, I believe, is just a key ingredient in doing research nowadays. Why? Because a lot of people are thinking directionally the same. And so, you know, if you’re not the first, you’re going to have to adapt to what came out. And then you have to think of, how do I differentiate? So the second point I would say is differentiation. And this mindset of, you know, how do I adapt to what just came out? How do I differentiate? And then—Rafah’s bias—how do I anchor in real-world scenario? This is the home run. And I would say you package all of this and focus, focus, focus … and you get a gold mine. 

HUIZINGA: I’m hearing “yes, and …” in this response in the sense of not everyone’s going to be first, but then, what else? This is back to the frontiers. It’s like, how do I differentiate? Yes, that’s awesome. And we’ve got this … 

HOSN: Exactly. And how do I build on what has just been discovered and give it a little bit of an edge or push it a little further or take it in a brand-new direction? I mean, so many different possibilities, but it does take adaptability, like a flexibility in the mindset, I would say. 

HUIZINGA: Yeah. Well, let’s go back to what you alluded to earlier, this idea of responsible AI. This is a big deal at Microsoft. And researchers are very thoughtful about the question of what could possibly go wrong if we got everything right. But how does that translate practically, and what concrete steps are you taking at what I’ll call the “frontier of responsibility?” 

HOSN: Yeah, and as I mentioned, you know, being at the frontiers is amazing. It also holds a big responsibility. We have so many different, I would say, checks and balances that we use, in model training and fine-tuning, to ensure that we are on top of all the regulatory, the policymaker suggestions, and we are abiding by Microsoft values first and foremost and responsibility in creating these innovations. So practically and tactically, what happens is that there are processes for how you actually even release any type of model. And this is just research. And when it goes to product, they have their own compliance, you know, a stricter even compliance, I would say, process that they go through. So we try, and I try particularly, to partner with our privacy champions, with our legal champions, with our people that are looking at this from a responsible AI perspective, so that we bring them in early on, and we say, hey, we’re thinking of doing this. And they tell us, well, you know, if you’re thinking about it this way, you might want to consider this. So we’re trying to bring them in as early as possible so that also we don’t go all the way and then we discover we did something wrong, so we have to backtrack. So I would say, you know, having these partners and colleagues come in early in the game just saves everybody a lot of time. And all this responsible AI for us, it’s ingrained with how we work, meaning we bring our champions early on and then we have them advise us as we move along the journey to create these innovations. So by the time we’re done, we know we’re good, right. And even by the time we’re done, we recheck everything, we run a lot of evaluation benchmarks, and, you know, we do the right thing per policies at Microsoft. So we take it very, very seriously. 

HUIZINGA: Well, let’s go back to this idea of research horizons for a second and anchor it in the way that we approach research. So many ideas are basically iterative steps on existing work, and they make a lot of sense … this is the next step … but then there are those out-of-the-box ideas that feel like maybe bigger swings—some might even call them outrageous—and in organizations like Microsoft Research, they might get the green light, too. Where do you find this idea of the outrageous or maybe longer-term idea finding a home or a place in an organization like Microsoft Research, and have you ever worked on something that felt outrageous to you? 

HOSN: Umm, you know, we like outrageous! That’s why we’re in research, right? So outrageous is good. I haven’t, to be honest, worked on an outrageous, but I am confident I will be. So … [LAUGHTER] I just have this belief that in AI Frontiers, we are going to have outrageous ideas, and we’re going to work on them, and we’re going to make bets that basically are hard to make in other parts of the company because we have the privilege of taking them and pursuing them. And, yes, they may fail, but if we have a breakthrough, it will be a significant breakthrough. So, so I think that outrageous is good. We need to think big. We need to take big leaps, big ideas. We also need to know how to fail gracefully and pivot fast! 

HUIZINGA: Hmmm. Mmm. You know, it strikes me, and I’m laughing to myself, it strikes me, even as we’re talking, that the idea that you work in AI Frontiers, that’s outrageous to most people and, and it’s normal to you. So maybe this idea of, “I haven’t worked on anything outrageous” is like, no, you live in outrageous, it just doesn’t seem like it! [LAUGHTER] 

HOSN: Maybe. It’s my day-to-day job, so, yes, I guess you’re right. 

HUIZINGA: Right. I mean, yeah, you say, we love outrageous, and that’s where it is right now. Every day that I follow, sort of, AI Twitter also and find myself going, seriously? That happened yesterday? What next? 

HOSN: Yeah, in two hours, there’ll be yet another thing. So, yeah, I guess I am living in outrageous, and I love it! It’s amazing! [LAUGHS] 

HUIZINGA: Yeah, maybe the idea of outrageous is just changed. 

HOSN: You know, you’re so right. I think that it’s become the norm. And it is, once we anchor in generative AI, and we push further on this idea, maybe we will go back in a cycle where outrageous is outrageous, but today it’s our life. It’s where we live. It’s what we breathe every day. So it’s become a norm. 

HUIZINGA: Yeah. Well, as we close, Rafah, I want to ask a question anchored on the big idea behind AI Frontiers. What do you believe might be true in say 10 to 15 years, and what should we be doing about it now? In other words, how does what we believe about the future influence how we conceptualize and execute on ideas today? 

HOSN: Yeah, you know, it’s … I can’t even predict what I’m going to be doing tomorrow! But … [LAUGHTER] here’s, here’s what I think. I think that we are truly approaching a moment in human history where a lot of unsurmountable problems, like very hard-to-tackle diseases that have been so hard, I think we are approaching a moment, you know, soon, I hope it’s even sooner than 10 years, where generative AI and innovations on top of it could lead to a lot of resolution for things that today … that cause unsurmountable pain and suffering. I’m very hopeful that with what we are creating that we can, you know, take inefficiencies out of so many different things that we see today that take time so that we liberate ourselves to think about the “what next” societally, right? I think what we need to be doing right now, to be honest, to influence the future is think about our curricula. What are we going to teach our kids? What are they going to work in? This is where I’m hoping that we pour some of our creativity, education system. How are we preparing the next generation? What are the paths that we are going to forge for them, knowing what we know today, knowing what this technology can bring forth? So my hope is that we put some brain power into that. 

HUIZINGA: Rafah Hosn, it’s always a pleasure to talk to you. A sincere pleasure, a delight. Thanks for joining us today on Ideas. 

HOSN: Thank you so much for having me, Gretchen. 

Transcript

GRETCHEN HUIZINGA: Welcome to Abstracts, a Microsoft Research Podcast that puts the spotlight on world-class research in brief. I’m Dr. Gretchen Huizinga. In this series, members of the research community at Microsoft give us a quick snapshot—or a podcast abstract—of their new and noteworthy papers.

I’m talking today to Tusher Chakraborty, a senior research software engineer at Microsoft Research. Tusher is coauthor of a paper called “Spectrumize: Spectrum-efficient Satellite Networks for the Internet of Things.” Tusher, thanks for joining us on Abstracts!

TUSHER CHAKRABORTY: Hi. Thank you for having me here, Gretchen, today. Thank you.

HUIZINGA: So because this show is all about abstracts, in just a few sentences, tell us about the problem your paper addresses and why we should care about it.

CHAKRABORTY: Yeah, so think of, I’m a farmer living in a remote area and bought a sensor to monitor the soil quality of my farm. The big headache for me would be how to connect the sensor so that I can get access to the sensor data from anywhere. We all know that connectivity is a major bottleneck in remote areas. Now, what if, as a farmer, I could just click the power button of the sensor, and it gets connected from anywhere in the world. It’s pretty amazing, right? And that’s what our research is all about. Get your sensor devices connected from anywhere in the world with just the click of power button. We call it one-click connectivity. Now, you might be wondering, what’s the secret sauce? It’s not magic; it’s direct-to-satellite connectivity. So these sensors directly get connected to the satellites overhead from anywhere on Earth. The satellites, which are orbiting around the earth, collect the data from the sensing devices and forward to the ground stations in some other convenient parts of the world where these ground stations are connected to the internet.

HUIZINGA: So, Tusher, tell us what’s been tried before to address these issues and how your approach contributes to the literature and moves the science forward.

CHAKRABORTY: So satellite connectivity is nothing new and has been there for long. However, what sets us apart is our focus on democratizing space connectivity, making it affordable for everyone on the planet. So we are talking about the satellites that are at least 10 to 20 times cheaper and smaller than state-of-the-art satellites. So naturally, this ambitious vision comes with its own set of challenges. So when you try to make something cheaper and smaller, you’ll face lots of challenges that all these big satellites are not facing. So if I just go a bit technical, think of the antenna. So these big satellite antennas, they can actually focus on particular part of the world. So this is something called beamforming. On the other hand, when we try to make the satellites cheaper and smaller, we can’t have that luxury. We can’t have beamforming capability. So what happens, they have omnidirectional antenna. So it seems like … you can’t focus on a particular part of the earth rather than you create a huge footprint on all over the earth. So this is one of the challenges that you don’t face in the state-of-the-art satellites. And we try to solve these challenges because we want to make connectivity affordable with cheaper and smaller satellites.

HUIZINGA: Right. So as you’re describing this, it sounds like this is a universal problem, and people have obviously tried to make things smaller and more affordable in the past. How is yours different? What methodology did you use to resolve the problems, and how did you conduct the research?

CHAKRABORTY: OK, I’m thrilled that you asked this one because the research methodology was the most exciting part for me here. As a part of this research, we launched a satellite in a joint effort with a satellite company. Like, this is very awesome! So it was a hands-on experience with a real-deal satellite system. It was not simulation-based system. The main goal here was to learn the challenge from a real-world experience and come up with innovative solutions; at the same time, evaluate the solutions in real world. So it was all about learning by doing, and let me tell you, it was quite the ride! [LAUGHTER] We didn’t do anything new when we launched the satellites. We just tried to see how industry today does this. We want to learn from them, hey, what’s the industry practice? We launched a satellite. And then we faced a lot of problems that today’s industry is facing. And from there, we learned, hey, like, you know, this problem is industry facing; let’s go after this, and let’s solve this. And then we tried to come up with the solutions based on those problems. And this was our approach. We didn’t want to assume something beforehand. We want to learn from how industry is going today and help them. Like, hey, these are the problems you are facing, and we are here to help you out.

HUIZINGA: All right, so assuming you learned something and wanted to pass it along, what were your major findings?

CHAKRABORTY: OK, that’s a very good question. So I was talking about the challenges towards this democratization earlier, right? So one of the most pressing challenges: shortage of spectrum. So let me try to explain this from the high level. So we need hundreds of these satellites, hundreds of these small satellites, to provide 24-7 connectivity for millions of devices around the earth. Now, I was talking, the footprint of a satellite on Earth can easily cover a massive area, somewhat similar to the size of California. So now with this large footprint, a satellite can talk with thousands of devices on Earth. You can just imagine, right? And at the same time, a device on Earth can talk with multiple satellites because we are talking about hundreds of these satellites. Now, things get tricky here. [LAUGHTER] We need to make sure that when a device and a satellite are talking, another nearby device or a satellite doesn’t interfere. Otherwise, there will be chaos—no one hearing others properly. So when we were talking about this device and satellite chat, right, so what is that all about? This, all about in terms of communication, is packet exchange. So the device sends some packet to the satellite; satellite sends some packet to the device—it’s all about packet exchange. Now, you can think of, if multiple of these devices are talking with a satellite or multiple satellites are talking with a device, there will be a collision in this packet exchange if you try to send the packets at the same time. And if you do that, then your packet will be collided, and you won’t be able to get any packet on the receiver end. So what we do, we try to send this packet on different frequencies. It’s like a different sound or different tone so that they don’t collide with each other. And, like, now, I said that you need different frequencies, but frequency is naturally limited. And the choice of frequency is even limited. This is very expensive. But if you have limited frequency and you want to resolve this collision, then you have a problem here. How do you do that? So we solve this problem by smartly looking at an artifact of these satellites. So these satellites are moving really fast around the earth. So when they are moving very fast around the earth, they create a unique signature on the frequency that they are using to talk with the devices on Earth. And we use this unique signature, and in physics, this unique signature is known as Doppler signature. And now you don’t need a separate frequency to sound them different, to have packets on different frequencies. You just need to recognize that unique signature to distinguish between satellites and distinguish between their communications and packets. So in that sense, there won’t be any packet collision. And this is all about our findings. So with this, now multiple devices and satellites can talk with each other at the same time without interference but using the same frequency.

HUIZINGA: It sounds, like, very similar to a big room filled with a lot of people. Each person has their own voice, but in the mix, you, kind of, lose track of who’s talking and then you want to, kind of, tune in to that specific voice and say, that’s the one I’m listening to.

CHAKRABORTY: Yeah, I think you picked up the correct metaphor here! This is the scenario you can try to explain here. So, yeah, like what we are essentially doing, like, if you just, in a room full of people and they are trying to talk with each other, and then if they’re using the same tone, no one will be distinguished one person from another.

HUIZINGA: Right …

CHAKRABORTY: Everyone will sound same and that will be colliding. So you need to make sure that, how you can differentiate the tones …

HUIZINGA: Yeah …

CHAKRABORTY: … and the satellites differentiate their tones due to their fast movement. And we use our methodology to recognize that tone, which satellite is sending that tone.

HUIZINGA: So you sent up the experimental satellite to figure out what’s happening. Have you since tested it to see if it works?

CHAKRABORTY: Yeah, yeah, so we have tried it out, because this is a software solution, to be honest.

HUIZINGA: Ah.

CHAKRABORTY: As I was talking about, there is no hardware modification required at this point. So what we did, we just implemented this software in the ground stations, and then we tried to recognize which satellite is creating which sort of signature. That’s it!

HUIZINGA: Well, it seems like this research would have some solid real-world impact. So who would you say it helps most and how?

CHAKRABORTY: OK, that’s a very good one. So the majority of the earth still doesn’t have affordable connectivity. The lack of connectivity throws a big challenge to critical industries such as agriculture—the example that I gave—energy, and supply chain, so hindering their ability to thrive and innovate. So our vision is clear: to bring 24-7 connectivity for devices anywhere on Earth with just a click of power button. Moreover, affordability at the heart of our mission, ensuring that this connectivity is accessible to all. So in core, our efforts are geared towards empowering individuals and industries to unlock their full potential in an increasingly connected world.

HUIZINGA: If there was one thing you want our listeners to take away from this research, what would it be?

CHAKRABORTY: OK, if there is one thing I want you to take away from our work, it’s this: connectivity shouldn’t be a luxury; it’s a necessity. Whether you are a farmer in a remote village or a business owner in a city, access to reliable, affordable connectivity can transform your life and empower your endeavors. So our mission is to bring 24-7 connectivity to every corner of the globe with just a click of a button.

HUIZINGA: I like also how you say every corner of the globe, and I’m picturing a square! [LAUGHTER] OK, last question. Tusher, what’s next for research on satellite networks and Internet of Things? What big unanswered questions or unsolved problems remain in the field, and what are you planning to do about it?

CHAKRABORTY: Uh … where do I even begin? [LAUGHTER] Like, there are countless unanswered questions and unsolved problems in this field. But let me highlight one that we talked here: limited spectrum. So as our space network expands, so does our need for spectrum. But what’s the tricky part here? Just throw more and more spectrum. The problem is the chunk of spectrum that’s perfect for satellite communication is often already in use by the terrestrial networks. Now, a hard research question would be how we can make sure that the terrestrial and the satellite networks coexist in the same spectrum without interfering [with] each other. It’s a tough nut to crack, but it’s a challenge we are excited to tackle head-on as we continue to push the boundaries of research in this exciting field.

HUIZINGA: Tusher Chakraborty, thanks for joining us today, and to our listeners, thanks for tuning in. If you want to read this paper, you can find a link at aka.ms/abstracts (opens in new tab). You can also read it on the Networked Systems Design and Implementation, or NSDI, website, and you can hear more about it at the NSDI conference this week. See you next time on Abstracts!

Transcript 

KALIKA BALI: I do think, in some sense, the pushback that I got for my idea makes me think it was outrageous. I didn’t think it was outrageous at all at that time! I thought it was a very reasonable idea! But there was a very solid pushback and not just from your colleagues. You know, for researchers, publishing papers is important! No one would publish a paper which focused only on, say, Indian languages or low-resource languages. We’ve come a very long way even in the research community on that, right. We kept pushing, pushing, pushing! And now there are tracks, there are workshops, there are conferences which are devoted to multilingual and low-resource languages. 

GRETCHEN HUIZINGA: You’re listening to Ideas, a Microsoft Research Podcast that dives deep into the world of technology research and the profound questions behind the code. I’m Dr. Gretchen Huizinga. In this series, we’ll explore the technologies that are shaping our future and the big ideas that propel them forward. 

I’m excited to be live in the booth today with Kalika Bali, a principal researcher at Microsoft Research India. Kalika is working on language technologies that she hopes will bring the benefits of generative AI to under-resourced and underserved language communities around the world. Kalika, it’s a pleasure to speak with you today. Welcome to Ideas! 

KALIKA BALI: Thank you. Thank you, Gretchen. Thank you for having me. 

HUIZINGA: So before we dive in on the big ideas behind Kalika Bali’s research, let’s talk about you for a second. Tell us about your “origin story,” as it were, and if there is one, what “big idea” or animating “what if?” captured your imagination and inspired you to do what you’re doing today? 

BALI: So, you know, I’m a great reader. I started reading well before I was taught in school how to read, and I loved science fiction. I come from a family where reading was very much a part of our everyday lives. My dad was a journalist, and I had read a lot of science fiction growing up, and I also saw a lot of science fiction, you know, movies … Star Trek … everything that I could get hold of in India. And I remember watching 2001: Space Odyssey. And there was this HAL that spoke. He actually communicated that he was a computer. And I was just so struck by it. I was like, this is so cool! You know, here are computers that can talk! Now, how cool would that be if it would happen in real life? I was not at all aware of what was happening in speech technology, whether it was possible or not possible, but that’s something that really got me into it. I’ve always, like, kind of, been very curious about languages and how they work and, you know, how people use different things in languages to express not just meaning, not just communicating, but you know expressing themselves, really. And so I think it’s a combination of HAL and this curiosity I had about the various ways in which people use languages that got me into what I’m doing now. 

HUIZINGA: OK. So that’s an interesting path, and I want to go into that just a little bit, but let me anchor this: how old were you when you saw this talking computer? 

BALI: Oh, I was in my early teens. 

HUIZINGA: OK. And so at that time, did you have any conception that … ? 

BALI: No. You know, there weren’t computers around me when I was growing up. We saw, you know, some at school, you know, people coded in BASIC … 

HUIZINGA: Right? 

BALI: And we heard about them a lot, but I hadn’t seen one since I was in high school. 

HUIZINGA: OK. So there’s this inception moment, an aha moment, of that little spark and then you kind of drifted away from the computer side of it, and what … tell us about how you went from there to that! 

BALI: So that, that’s actually a very funny story because I actually wanted to study chemistry. I was really fascinated by how these, you know, molecular parts rotate around each other and, you know, we can’t even tell where an electron is, etc. It sounded, like, really fun and cool. So I actually studied chemistry, but then I was actually going to pick up the admission form for my sister, who wanted to study in this university, and … or, no, she wanted to take an exam for her master’s. And I went there. I picked up the form, and I said, this is a cool place. I would love to study here! And then I started looking at everything like, you know, what can I apply for here? And something called linguistics came up, and I had no idea what linguistics was. So I went to the British Library, got like a thin book on introduction to linguistics, and it sounded fun! And I took the exam. And then, as they say, that was history. Then I just got into it. 

HUIZINGA: OK. I mean, so much has happened in between then and now, and I think we’ll kind of get there in … but I do want you to connect the larger dot from how you got from linguistics to Microsoft Research [LAUGHTER] as a computer scientist.

BALI: So I actually started teaching at the University of South Pacific as a linguistics faculty in Fiji. And I was very interested in acoustics of speech sounds, etc., etc. That’s what I was teaching. And then there was a speech company in Belgium that was looking to start some work in Indian languages, and they contacted me, and at that time, you needed people who knew about languages to build language technology, especially people who knew about phonetics, acoustics, for speech technology. And that’s how I got into it. And then, you know, I just went from startups to companies and then Microsoft Research, 18 years ago, almost 18 years ago. 

HUIZINGA: Wow. OK. I would love to actually talk to you about all that time. But we don’t have time because I have a lot more things to talk to you about, technology-wise. But I do want to know, you know, how would you describe the ideas behind your overarching research philosophy, and who are your influences, as they say in the rock-and-roll world? [LAUGHTER] Who inspired you? Real-life person, scientist or not, besides, HAL 9000, who’s fictional, and any seminal papers that, sort of, got you interested in that along the way? 

BALI: So since I was really into speech, Ken Stevens—who was a professor, who sadly is no longer with us anymore, at MIT—was a big influence. He, kind of, had this whole idea of how speech is produced. And, you know, the first time I was exposed to the whole idea of the mathematics behind the speech, and I think he influenced me a lot on the speech side of things. For the language side of things, you know, my professor in India Professor Anvita Abbi—you know, she’s a Padma Shri, like, she’s been awarded by the Indian government for her work in, you know, very obscure, endangered languages—you know, she kind of gave me a feel for what languages are, and why they are important, and why it’s important to save them and not let them die away. 

HUIZINGA: Right.

BALI: So I think I would say both of them. But what really got me into wanting to work with Indian language technology in a big way was I was working in Belgium, I was working in London, and I saw the beginning of how technology is, kind of, you know, making things easier, exciting; there’s cool technology available for English, for French, for German … But in a country like India, it was more about giving access to people who have no access, right? It actually mattered, because here are people who may not be very literate and therefore not be able to use technology in the way we know it, but they can talk. 

HUIZINGA: Right. 

BALI: And they can speak, and they should be able to access technology by doing that. 

HUIZINGA: Right. OK. So just real quickly, that was then. What have you seen change in that time, and how profoundly have the ideas evolved? 

BALI: So just from pure methodology and what’s possible, you know, I have seen it all. When I started working in language technology, mainly for Indian languages, but even for other languages, it was all a rule-based system. So everybody had to create all these rules that then were, you know, responsible for building or like making that technology work. But then, just at that time, you know, all the statistical systems and methodologies came into being. So we had hidden Markov models, you know, doing their thing in speech, and it was all about a lot of data. But that data still had to be procured in a certain way, labeled, annotated. It was still a very long and resource-intensive process. Now, with generative AI, the thing that I am excited about is, we have a very powerful tool, right? 

HUIZINGA: Mm-hmm. 

BALI: And, yes, it requires a lot of data, but it can learn also; you know, we can fine-tune stuff on smaller datasets … 

HUIZINGA: Yeah … 

BALI: … to work for, you know, relevant things. So it’s not going to take me years and years and years to first procure the data, then have it tagged for part of speech … then, you know, have it tagged for sentiment, have it tagged for this, have it tagged for that, and then, only can I think of building anything. 

HUIZINGA: Right.

BALI: So it just shortens that timeline so much, and it’s very exciting. 

HUIZINGA: Right. As an ex-English teacher—which I don’t think there is such a thing as an ex-English teacher; you’re always silently correcting someone’s grammar! [LAUGHTER]—just what you said about tagging parts of speech as what they are, right? And that, I used to teach that. And then you start to think, how would you translate that for a machine? So fascinating. So, Kalika, you have said that your choice of career was accidental—and you’ve alluded to the, sort of, the fortuitous things that happened along the way—but that linguistics is one subject that goes from absolute science to absolute philosophy. Can you unpack that a little bit more and how this idea impacted your work in language technology? 

BALI: Yeah. So, so if you think about it, you know, language has a physical aspect, right. We move our various speech organs in a certain way. Our ears are constructed in a certain way. There is a physics of it where, when I speak, there are sound waves, right, which are going into your ear, and that’s being interpreted. So, you know, if you think about that, that’s like an absolute science behind it, right? But then, when you come to the structure of language, you know, the syntax, like you’re an English teacher, so you know this really well, that you know, there’s semantics; there’s, you know, morphology, how our words form, how our sentences form. And that’s like a very abstract kind of method that allows us to put, you know, meaningful sentences out there, right? 

HUIZINGA: Right … 

BALI: But then there’s this other part of how language works in society, right. The way I talk to my mother would be probably very different to the way I’m talking to you, would be very different from the way I talk to my friends, at a very basic level, right? The way, in India, I would greet someone older to me would be very different from the way I would greet somebody here, because here it’s like much less formal and that, you know, age hierarchy is probably less? If I did the same thing in India, I would be considered the rudest creature ever. [LAUGHS] So … and then, you know, you go into the whole philosophy—psycholinguistics part. What happens in our brains, you know, when we are speaking? Because language is controlled by various parts of our brain, right. And then, you go to the pure philosophy part, like why? How does language even occur? Why do we name things the way we name things? You know, why do we have a language of thought? You know, what language are we thinking in? [LAUGHTER] 

HUIZINGA: Right. 

BALI: So, so it really does cover the entire gamut of language … 

HUIZINGA: Yeah, yeah, yeah … 

BALI: … like from science to philosophy. 

HUIZINGA: Yeah, as I said before, when we were talking out there, my mother-in-law was from Holland, and every time she did math or adding, she would do it in Dutch, which—she’d be speaking in English and then she’d go over here and count in Dutch out loud. And it’s like, yeah, your brain switches back and forth. This is so exciting to me. I had no idea how much I would love this podcast! So, much of your research is centered on this big idea called “design thinking,” and it’s got a whole discipline in universities around the world. And you’ve talked about using something you call the 4D process for your work. Could you explain that process, and how it plays out in the research you do with the communities you serve?

BALI: Yeah, so we’ve kind of adapted this. My ex-colleague Monojit Choudhury and I, kind of, came up with this whole thing about 4D thinking, which is essentially discover, design, develop and deploy, right. And when we are working with, especially with, marginalized or low-resource-language communities, the very basic thing we have to do is discover, because we cannot go with, you know, our own ideas and perceptions about what is required. And I can give you a very good example of this, right. You know, most of us, as researchers and technologists, when we think of language technology, we are thinking about machine translation; we’re thinking about speech recognition; we are thinking about state-of-the-art technology. And here we were talking to a community that spoke the language Idu Mishmi, which is a very small community in northeast of India. And we were talking about, you know, we can do this, we can do that. And they just turned to us and said, what we really want is a mobile digital dictionary! [LAUGHS] 

HUIZINGA: Wow. Yeah … 

BALI: Right? And, you know, if you don’t talk, if you don’t observe, if you are not open to what the community’s needs might be, then you’ll miss that, right. You’ll miss the real thing that will make a difference to that community. So that’s the discover part. The design part, again, you have to design with the community. You cannot go and design a system that they are unable to use properly, right. And again, another very good example, one of the people I know, you know, he gave me this very good example of why you have to think, even at the architecture level when you’re designing such things, is like a lot of applications in India and around the world require your telephone number for verification. Now, for women, it might be a safety issue. They might not want to give their telephone number. Or in India, many women might not even have a telephone, like a mobile number, right. So how do you think of other ways in which they can verify, right? And so that’s the design part. The develop and the deploy part, kind of, go hand in hand, because I think it’s a very iterative process. You develop quickly, you put it out there, allow it to fail and, you know … 

HUIZINGA: Mm-hmm. Iterate … 

BALI: Iterate. So that’s like the, kind of, design thinking that we have. 

HUIZINGA: Yeah, I see that happening in accessibility technology areas, too, as well as language … 

BALI: Yeah, and, you know, working with the communities, very quickly, you become really humble.

HUIZINGA: Sure.

BALI: There’s a lot of humility in me now. Though I have progressed in my career and, you know, supposedly become wiser, I am much more humble about what I know and what I can do than I was when I started off, you know. 

HUIZINGA: I love that. Well, one thing I want to talk to you about that has intrigued me, there’s a thing that happens in India where you mix languages … 

BALI: Yes!

HUIZINGA: You speak both Hindi and English at the same time, and you think, oh, you speak English, but it’s like, no, there’s words I don’t understand in that. What do you call that, and how did that drive your interest? I mean, that was kind of an early-on kind of thing in your work, right? Talk about that. 

BALI: So that’s called code-mixing or code-switching. The only linguistic difference is code-mixing happens within a sentence, and code-switching means one sentence in one language and another. 

HUIZINGA: Oh, really? 

BALI: Yeah. So … but this is, like, not just India. This is a very, very common feature of multilingual societies all over the world. So it’s not multilingual individuals, but at the societal level, when you have multilingualism, then, you know, this is a marker of multilingualism. But code-mixing particularly means that you have to be fluent in both languages to actually code-mix, right. You have to have a certain amount of fluency in both languages. And there are various reasons why people do this. You know, it’s been studied by psychologists and linguists for a long time. And for most people like me, multilingual people, that’s the language we dream in, we think about. [LAUGHTER] That’s the language we talk to our siblings and friends in, right. And for us, it’s, like, just natural. We just keep … 

HUIZINGA: Mixing … 

BALI: … flipping between the two languages for a variety of reasons. We might do it for emphasis; we might do it for humor. We might just decide, OK, I’m going to pick this from this … the brain decides I’m going to pick this from this language … 

HUIZINGA: Sure. 

BALI: … and this … So the reason we got interested in, like, looking into code-mixing was that when we are saying that we want humans to be able to interact with machines in their most natural language, then by some estimates, half the world speaks like this! 

HUIZINGA: Right. 

BALI: So we have to be able to understand exactly how they speak and, you know, be able to process and understand their language, which is code-mixed … 

HUIZINGA: Sure. Well, it seems like the human brain can pick this up and process it fairly quickly and easily, especially if it knows many languages. For a machine, it would be much more difficult? 

BALI: It is. So initially, it was really difficult because, you know, the way we created systems was one language at a time … 

HUIZINGA: Right! 

BALI: … right. And it’s not about having an English engine and a Hindi engine available. It doesn’t work that way. 

HUIZINGA: No!

BALI: So you’d really need something that, you know, is able to tackle the languages together. And in some theories, this is almost considered a language of its own because it’s not like you’re randomly mixing. There is a structure to … 

HUIZINGA: Oh, is there? 

BALI: Yeah. Where you can, where you can’t … 

HUIZINGA: Gotcha. 

BALI: You know, so there is a structure or grammar, you can say, of code-mixing. So we went after that. We, kind of, created tools which could generate grammatically viable code-mixed sentences given parallel data, etc. 

HUIZINGA: That’s awesome. Amazing.

BALI: So, yeah, it takes effort to do it. But again, right now, because the generative AI models have at their disposal, you know, so many languages and at least, like, theoretically can work in many, many, many languages, you know, code-mixing might be an easier problem to solve right now. 

HUIZINGA: Right. OK. So we’re talking mostly about widely used languages, and you’re very concerned right now on this idea of low-resource languages. So unpack what you mean by low-resource, and what’s missing from the communities that speak those languages? 

BALI: Yeah. So when we say low-resource languages, we typically mean that languages do not have, say, digital resources, linguistic resources, language resources, that would enable technology building. It doesn’t mean that the communities themselves are impoverished in culture or linguistic richness, etc., right. But the reason why these communities do not have a lot of language resources, linguistic resources, digital resources, most of the time, it is because they are also marginalized in other ways … social and economic marginalization. 

HUIZINGA: Right. 

BALI: And these are … if you look at them, they’re not ti—I mean, of course, some of them are tiny, but when we say low-resource communities, we are talking about really big numbers. 

HUIZINGA: Oh, really? 

BALI: Yeah. So one of the languages that I have worked with—language communities that I’ve worked with—speak a language called Gondi, which is like a Dravidian language that is spoken in … like a South Indian language that is spoken in north, central-north area. It’s a tribal language, and it’s got around three million speakers.

HUIZINGA: Oh, wow! 

BALI: Yeah. That’s like more than Welsh, … 

HUIZINGA: Yeah! [LAUGHS] 

BALI: … right? But because socio-politically, they have been—or economically, they have been marginalized, they do not have the resources to build technologies. And, you know, when we say empower everyone and we only empower the top tier, I don’t think we fulfill our ambition to empower everyone. And like I said earlier, for these communities, all the technology that we have, digital tools that we have access to, they really matter for them. So, for example, you know, a lot of government schemes or the forest reserve laws are provided, say, in Hindi. If they are provided in Gondi, these people have a real idea of what they can do. 

HUIZINGA: Yeah. … Sure. 

BALI: Similarly, for education, you know, there are books and books and books in Hindi. There’s no book available for Gondi. So how is the next generation even going to learn the language? 

HUIZINGA: Right. 

BALI: And there are many, many languages which are low resource. In fact, you know, we did a study sometime in 2020, I think, we published this paper on linguistic diversity, and there we saw that, you know, we divided languages in five categories, and the top most which have all the resources to build every possible technology have only five languages, right. And more than half of the world’s languages are at the bottom. So it is a big problem. 

HUIZINGA: Yeah. Let’s talk about some of the specific technologies you’re working on. And I want to go from platform to project because you’ve got a big idea in a platform you call VeLLM. Talk about that. 

BALI: So VeLLM, which actually means jaggery—the sweet, sugary jaggery—in Tamil, one of the languages in India … 

HUIZINGA: Let me, let me interject that it’s not vellum like the paper, or what you’re talking about. It’s capital V, little e, and then LLM, which stands for large language model? 

BALI: So universal, the “V” comes from there. Empowerment, “e” comes from there. Through large language models … 

HUIZINGA: Got it. OK. But you shortened it to VeLLM. 

BALI: Yeah. 

HUIZINGA: OK.

BALI: So, so the thing with VeLLM is that a bunch of us got together just when this whole GPT was released, etc. We have a very strong group that works on technologies for empowerment in the India lab, Microsoft Research India. And we got together to see what it is that we can do now that we have access to such a strong and powerful tool. And we started thinking of the work that we’ve been doing, which is to, you know, build these technologies for specific areas and specific languages, specific demographies. So we, kind of, put all that knowledge and all that experience we had and thought of like, how can we scale that, really, across everything that we do? So VeLLM, at its base, you know, takes a GPT-like LLM, you know, as a horizontal across everything. On top of it, we have again, horizontals of machine learning, of multilingual tools and processes, which allow us to take the outputs from, say, GPT-like things and adapt it to different languages or, you know, some different kind of domain, etc. And then we have verticals on top of it, which allow people to build specific applications. 

HUIZINGA: Let me just go back and say GPT … I think most of our audience will know that that stands for generative pretrained transformer models. But just so we have that for anyone who doesn’t know, let’s anchor that. So VeLLM basically was an enabling platform … 

BALI: Yes. 

HUIZINGA: … on which to build specific technologies that would solve problems in a vertical application. 

BALI: Yes. Yes. And because it’s a platform, we’re also working on tools that are needed across domains … 

HUIZINGA: Oh, interesting. 

BALI: … as well as tools that are needed for specific domains. 

HUIZINGA: OK, so let’s talk about some of the specifics because we could get into the weeds on the tools that everybody needs, but I like the ideas that you’re working on and the specific needs that you’re meeting, the felt-need thing that gets an idea going. So talk about this project that you’ve worked on called Kahani. Could you explain what that is, and how it works? It’s really interesting to me. 

BALI: So Kahani, actually, is about storytelling, culturally appropriate storytelling, with spectacular images, as well as like textual story. 

HUIZINGA: So visual storytelling? 

BALI: Visual storytelling with the text. So this actually started when my colleague Sameer Segal, he was trying to use generative AI to create stories for his daughter, and he discovered that, you know, things are not very culturally appropriate! So I’ll give an example that, you know, if you want to take Frozen and take it to, like, the south Indian state of Kerala, you’ll have the beaches of Kerala, you’ll have even have the coconut trees, but then you will have this blond princess in a princess gown … 

HUIZINGA: Sure …

BALI: … who’s there, right? So that’s where we started discussing this, and we, kind of, started talking about, how can we create visuals that are anchored on text of a story that’s culturally appropriate? So when we’re talking about, say, Little Red Riding Hood, if we ask the generative AI model, OK, that I want the story of Little Red Riding Hood but in an Indian context, it does a fantastic job. It actually gives you a very nice story, which, you know, just reflects the Red Riding Hood story into an Indian context. But the images don’t really … 

HUIZINGA: Match … [LAUGHTER] 

BALI: … Match at all. So that’s where the whole Kahani thing started. And we did a hackathon project on it. And then a lot of people got interested. It’s an ongoing project, so I won’t say that it’s out there yet, but we are very excited about it, but because think of it, we can actually create stories for children, you know, which is what we started with, but we can create so much more media, so much more culturally appropriate storytelling, which is not necessarily targeted at children. 

HUIZINGA: Yeah, yeah. 

BALI: So that’s what Kahani is about. 

HUIZINGA: OK. And I saw a demo of it that your colleague did for Research Forum here, and there was an image of a girl—it was beautiful—and then there was a mask of some kind or a … what was that? 

BALI: So the mask is called Nazar Battu, which is actually, you have these masks which are supposed to drive away the evil eye. So that’s what the mask was about. It’s a very Indian thing. You know, when you build a nice house, you put one on top of it so that the envious glances are, like, kept at bay. So, yeah, so that’s what it was. 

HUIZINGA: And was there some issue of the generative AI not really understanding what that was? 

BALI: No, it didn’t understand what it was. 

HUIZINGA: So then can you fix that and make it more culturally aware? 

BALI: So that’s what we are trying to do for the image thing. So we have another project on culture awareness where we are looking at understanding how much generative AI knows about other cultures. 

HUIZINGA: Interesting. 

BALI: So that’s a simultaneous project that’s happening. But in Kahani, a lot of it is, like, trying to get reference images, you know … 

HUIZINGA: Yeah. … Into the system? 

BALI: Into the system … 

HUIZINGA: Gotcha … 

BALI: … and trying to anchor on that. 

HUIZINGA: Mmmm. So—and we’re not going to talk about that project, I don’t think—but … how do you assess whether an AI knows? By just asking it? By prompting and seeing what happens? 

BALI: Yeah, yeah, yeah. So in another project, what we did was, we asked humans to play a game to get cultural artifacts from them. The problem with asking humans what cultural artifacts are important to them is we don’t think of like things as culture, right. [LAUGHS] This is food! 

HUIZINGA: It’s just who we are! 

BALI: This is my food. Like, you know, it’s not a culturally important artifact. This is how I greet my parents. It’s not like culturally … 

HUIZINGA: So it’s just like fish swimming in water. You don’t see the water. 

BALI: Exactly. So we gamified this thing, and we were able to get certain cultural artifacts, and we tried to get generative AI models to tell us about the same artifacts. And it didn’t do too well … [LAUGHS] 

HUIZINGA: But that’s why it’s research! 

BALI: Yes! 

HUIZINGA: You try, you iterate, you try again … cool. As I mentioned earlier, I was a high school English teacher and an English major. I’m not correcting your grammar because it’s fantastic.

BALI: Thank you. 

HUIZINGA: But as a former educator, one of the projects I felt was really compelling that you’re working on is called Shiksha. It’s a copilot in education. Tell our audience about this.

BALI: So this is actually our proof of concept for the VeLLM platform. Since almost all of us were interested in education, we decided to go for education as the first use case that we’re going to work on. And actually, it was a considered decision to go target teachers instead of students. I mean, you must have seen a lot of work being done on taking generative AI to students, right. But we feel that, you know, teachers are necessary to teach because they’re not just giving you information about the subject. They’re giving you skills to learn, which hopefully will stay with you for a lifetime, right. And if we enable teachers, they will enable so many hundreds of students. One teacher can enable thousands of students, right, over her career. So instead of, like, going and targeting students, if we make it possible for teachers to do their jobs more effectively or, like, you know, help them get over the problems they have, then we are actually creating an ecosystem where things will scale really fast, really quickly. And in India, you know, this is especially true because the government has actually come up with some digital resources for teachers to use, but there’s a lot more that can be done. So we interviewed about a hundred-plus teachers across different parts of the country. And this is the, you know, discover part. 

HUIZINGA: Yeah! 

BALI: And we found out that lesson plans are a big headache! [LAUGHS] 

HUIZINGA: Yes, they are! Can confirm! 

BALI: Yeah. And they spend a lot of time doing lesson plans because they’re required to create a lesson plan for every class they teach … 

HUIZINGA: Sure. With learning outcomes … 

BALI: Exactly. 

HUIZINGA: All of it. 

BALI: All of it. So that’s where we, you know, zeroed in on—how to make it easier for teachers to create lesson plans. And that’s what the Shiksha project is about. You know, there is an enrollment process where the teachers say what subject they’re teaching, what classes they’re teaching, what boards, because there are different boards of education … 

HUIZINGA: Right … 

BALI: … which have different syllabus. So all that. But after that, it takes less than seven minutes for a teacher to create an entire lesson plan for a particular topic. You know, class assignments, class activities, home assignments, homework—everything! Like the whole thing in seven minutes! And these teachers have the ability to go and correct it. Like, it’s an interactive thing. So, you know, they might say, I think this activity is too difficult for my students. 

HUIZINGA: Yeah … 

BALI: Can I have, like, an easier one? Or, can I change this to this? So it allows them to interactively personalize, modify the plan that’s put out. And I find that really exciting. And we’ve tested this with the Sikshana Foundation, which works with teachers in India. We’ve tested this with them. The teachers are very excited and now Sikshana wants to scale it to other schools. 

HUIZINGA: Right … well, my first question is, where were you when I was teaching, Kalika? 

BALI: There was no generative AI! 

HUIZINGA: No. In fact, we just discovered the fax machine when I started teaching. Oh, that dates me! You know, back to what you said about teachers being instrumental in the lives of their students. You know, we can remember our favorite teachers, our best teachers. We don’t remember a machine. 

BALI: No.

HUIZINGA: And what you’ve done with this is to embody the absolute sort of pinnacle of what AI can do, which is to be the collaborator, the assistant, the augmenter, and the helper so that the teacher can do that inspirational, connective-tissue job with the students without having to, like, sacrifice the rest of their life making lesson plans and grading papers. Oh, my gosh. OK. On the positive side, we’ve just talked about what this work proposes and how it’s good, but I always like to dig a little bit into the potential unintended consequences and what could possibly go wrong if, in fact, you got everything right. So I’ll anchor this in another example. When GPT models first came out, the first reaction came from educators. It feels like we’re in a bit of a paradigm shift like we were when the calculator and the internet even came out. [It’s] like, how do we process this? So I want to go philosophically here and talk about how you foresee us adopting and moving forward with generative AI in education, writ large. 

BALI: Yeah, I think this is a question that troubles a lot of us and not just in education, but in all spheres that generative AI is … 

HUIZINGA: Art … 

BALI: … art … 

HUIZINGA: … writing … 

BALI: … writing … 

HUIZINGA: … journalism … 

BALI: Absolutely. And I think the way I, kind of, think about it in my head is it’s a tool. At the end of it, it is a tool. It’s a very powerful tool, but it is a tool, and humans must always have the agency over it. And we need to come up, as a society, you know, we need to come up with the norms of using the tool. And if you think about it, you know, internet, taking internet as an example, there is a lot of harm that internet has propagated, right. The darknet and all the other stuff that happens, right. But on the whole, there are regulations, but there are also an actual consensus around what constitutes the positive use of internet, right. 

HUIZINGA: Sure, yeah. 

BALI: Nobody says that, for example, deepfakes are … 

HUIZINGA: Mm-hmm. Good … 

BALI: … good, right. So we have to come from there and think about what kind of regulations we need to have in place, what kind of consensus we need to have in place, what’s missing. 

HUIZINGA: Right. Another project that has been around, and it isn’t necessarily on top of VeLLM, but it’s called Karya, and you call it a social impact organization that serves not just one purpose, but three. Talk about that. 

BALI: Oh, Karya is my favorite! [LAUGHS] So Karya started as a research project within Microsoft Research India, and this was the brainchild again of my colleague—I have like some of the most amazing colleagues, too, that I work with!—called Vivek Seshadri. And Vivek wanted to create, you know, digital work for people who do not have access to such work. So he wanted to go to the rural communities, to people who belong to slightly lower socioeconomic demographies, and provide work, like microtasks kind of work, gig work, to them. And he was doing this, and then we started talking, and I said, you know, we need so much data for all these languages and all these different tasks, and that could be, like, a really cool thing to try on Karya, and that’s where it all started, my involvement with Karya, which is still pretty strong. And Karya then became such a stable project that Microsoft Research India spun it out. So it’s now its own standalone startup right now like a social enterprise, and they work on providing digital work. They work on providing skills, like upskilling. They work on awareness, like, you know, making people aware of certain social, financial, other such trainings. So what’s been most amazing is that Karya has been able to essentially collect data for AI in the most ethical way possible. They pay their workers a little over the minimal wage. They also have something called data ownership practice, where the data that is created by, say, me, I have some sort of ownership on it. So what that means is that every time Karya sells a dataset, a royalty comes back … 

HUIZINGA: No … ! 

BALI: Yeah! To the workers. 

HUIZINGA: OK, we need to scale this out! [LAUGHS] OK. So to give a concrete example, the three purposes would be educational, financial—on their end—and data collection, which would ultimately support a low-resource language by having digital assets.

BALI: Absolutely! 

HUIZINGA: So you could give somebody something to read in their language … 

BALI: Yeah. 

HUIZINGA: … that would educate them in the process. They would get paid to do it, and then you would have this data. 

BALI: Yes! 

HUIZINGA: OK. So cool. So simple. 

BALI: Like I said, it’s my favorite project. 

HUIZINGA: I get that. I totally get that. 

BALI: And they … they’ve been, you know, they have been winning awards and things all over for the work that they’re doing right now. And I am very involved in one project with them, which is to do with gender-intentional AI, or gender-intentional datasets for AI, for Indian languages. And that’s really crucial because, you know, we talk about gender bias in datasets, etc., but all that understanding comes from a very Western perspective and for languages like English, etc. They do not translate very well to Indian languages. 

HUIZINGA: Right. 

BALI: And in this particular project, we’re looking at first, how to define gender bias. How do we even get data around gender bias? What does it even mean to say that technology is gender intentional? 

HUIZINGA: Right. All right, well, let’s talk a little bit about what I like to call outrageous ideas. And these are the ones that, you know, on the research spectrum from sort of really practical applied research to blue sky get dismissed or viewed as unrealistic or unattainable. So years ago—here’s a little story about you—when you told your tech colleagues that you wanted to work with the world’s most marginalized languages, they told you you’d only marginalize yourself. 

BALI: Yes! 

HUIZINGA: But you didn’t say no. You didn’t say no. Um, two questions. Did you feel like your own idea was outrageous back then? And do you still have anything outrageous yet to accomplish in this plan? 

BALI: Oh, yeah! I hope so! Yeah. No, I do think, in some sense, the pushback that I got for my idea makes me think it was outrageous. I didn’t think it was outrageous at all at that time! [LAUGHS] I thought it was a very reasonable idea! But there was a very solid pushback and not just from your colleagues. You know, for researchers, publishing papers is important! No one would publish a paper which focused only on, say, Indian languages or low-resource languages. We’ve come a very long way even in the research community on that, right. We kept pushing, pushing, pushing! And now, there are tracks, there are workshops, there are conferences which are devoted to multilingual and low-resource languages. When I said I wanted to work on Hindi, and Hindi is the biggest language in India, right. And even for that, I was told, why don’t you work on German instead? And I’m like, there are lots of people working on German who will solve the problems with German! Nobody is looking at Hindi! I mean, people should work on all the languages. People should work on German, but I don’t want to work on German! So there was a lot of pushback back then, and I see a little bit of that with the very low-resource languages even now. And I think some people think it’s a “feel-good” thing, whereas I think it’s not. I think it’s a very economically viable, necessary thing to build technology for these communities, for these languages. No one thought Hindi was economically viable 15 years ago, for whatever reason … 

HUIZINGA: That … that floors me … 

BALI: Yeah, but, you know, we’re not talking about tens of thousands of people in some of these languages; we’re talking about millions. 

HUIZINGA: Yeah. 

BALI: I still think that is a job that I need to continue, you know, pushing back on. 

HUIZINGA: Do you think that any of that sort of outrageous reaction was due to the fact that the technology wasn’t as advanced as it is now and that it might have changed in terms of what we can do? 

BALI: There was definitely the aspect of technology there that it was just quite difficult and very, very resource-intensive to build it for languages which did not have resources. You know, there was a time when we were talking about how to go about doing this, and because people in various big tech companies, people did not really remember a time when, for English, they had to start data collection from scratch because everyone who was working on, say, English at that time was building on what people had done years and years ago. So they could not even conceptualize that you had to start from scratch for anything, right. But now with the technology as well, I’m quite optimistic and trying to think of how cool it would be to do, you know, smaller data collections and fine-tuned models specifically and things like that, so I think that the technology is definitely one big thing, but economics is a big factor, too. 

HUIZINGA: Mmm-hmm. Well, I’m glad that you said it isn’t just the feel good, but it actually would make economic sense because that’s some of the driver behind what technologies get “greenlit,” as it were. Is there anything outrageous now that you could think of that, even to you, sounds like, oh, we could never do that … 

BALI: Well … I didn’t think HAL was outrageous, so I’m not … [LAUGHS] 

HUIZINGA: Back to HAL 9000! [LAUGHS] 

BALI: Yeah, so I don’t think of things as outrageous or not. I just think of things as things that need to get done, if that makes any sense? 

HUIZINGA: Totally. Maybe it’s, how do we override “Open the pod bay door, HAL”—“No, I’m sorry, Dave. I can’t do that”? [LAUGHS] 

BALI: Yes. [LAUGHS] Yeah… 

HUIZINGA: Well, as we close—and I’m sad to close because you are so much fun—I want to do a little vision casting, but in reverse. So let’s fast-forward 20 years and look back. How have the big ideas behind your life’s work impacted the world, and how are people better off or different now because of you and the teams that you’ve worked with? 

BALI: So the way I see it is that people across the board, irrespective of the language they speak, the communities they belong to, the demographies they represent, can use technology to make their lives, their work, better. I know it sounds like really a very big and almost too good to be true, but that’s what I’m aiming for. 

HUIZINGA: Well, Kalika Bali, I’m so grateful I got to talk to you in person. And thanks for taking time out from your busy trip from India to sit down with me and our audience and share your amazing ideas. 

BALI: Thank you so much, Gretchen.

Transcript

ASHLEY LLORENS: I’m Ashley Llorens with Microsoft Research. In this podcast series, I share conversations with fellow researchers about the latest developments in AI models, the work we’re doing to understand their capabilities and limitations, and ultimately how innovations like these can have the greatest benefit for humanity. Welcome to AI Frontiers.

Today, I’ll speak with Ida Momennejad. Ida works at Microsoft Research in New York City at the intersection of machine learning and human cognition and behavior. Her current work focuses on building and evaluating multi-agent AI architectures, drawing from her background in both computer science and cognitive neuroscience. Over the past decade, she has focused on studying how humans and AI agents build and use models of their environment.

Let’s dive right in. We are undergoing a paradigm shift where AI models and systems are starting to exhibit characteristics that I and, of course, many others have described as more general intelligence. When I say general in this context, I think I mean systems with abilities like reasoning and problem-solving that can be applied to many different tasks, even tasks they were not explicitly trained to perform. Despite all of this, I think it’s also important to admit that we—and by we here, I mean humanity—are not very good at measuring general intelligence, especially in machines. So I’m excited to dig further into this topic with you today, especially given your background and insights into both human and machine intelligence. And so I just want to start here: for you, Ida, what is general intelligence?

IDA MOMENNEJAD: Thank you for asking that. We could look at general intelligence from the perspective of history of cognitive science and neuroscience. And in doing so, I’d like to mention its discontents, as well. There was a time where general intelligence was introduced as the idea of a kind of intelligence that was separate from what you knew or the knowledge that you had on a particular topic. It was this general capacity to acquire different types of knowledge and reason over different things. And this was at some point known as g, and it’s still known as g. There have been many different kinds of critiques of this concept because some people said that it’s very much focused on the idea of logic and a particular kind of reasoning. Some people made cultural critiques of it. They said it’s very Western oriented. Others said it’s very individualistic. It doesn’t consider collective or interpersonal intelligence or physical intelligence. There are many critiques of it. But at the core of it, there might be something useful and helpful. And I think the useful part is that there could be some general ability in humans, at least the way that g was intended initially, where they can learn many different things and reason over many different domains, and they can transfer ability to reason over a particular domain to another. And then in the AGI, or artificial general intelligence, notion of it, people took this idea of many different abilities or skills for cognitive and reasoning and logic problem-solving at once. There have been different iterations of what this means in different times. In principle, the concept in itself does not provide the criteria on its own. Different people at different times provide different criteria for what would be the artificial general intelligence notion. Some people say that they have achieved it. Some people say we are on the brink of achieving it. Some people say we will never achieve it. However, there is this idea, if you look at it from an evolutionary and neuroscience and cognitive neuroscience lens, that in evolution, intelligence has evolved multiple times in a way that is adaptive to the environment. So there were organisms that needed to be adaptive to the environment where they were, that intelligence has evolved in multiple different species, so there’s not one solution to it, and it depends on the ecological niche that that particular species needed to adapt to and survive in. And it’s very much related to the idea of being adaptive of certain kinds of, different kinds of problem-solving that are specific to that particular ecology. There is also this other idea that there is no free lunch and the no-free-lunch theorem, that you cannot have one particular machine learning solution that can solve everything. So the idea of general artificial intelligence in terms of an approach that can solve everything and there is one end-to-end training that can be useful to solve every possible problem that it has never seen before seems a little bit untenable to me, at least at this point. What does seem tenable to me in terms of general intelligence is if we understand and study, the same way that we can do it in nature, the foundational components of reasoning, of intelligence, of different particular types of intelligence, of different particular skills—whether it has to do with cultural accumulation of written reasoning and intelligence skills, whether it has to do with logic, whether it has to do with planning—and then working on the particular types of artificial agents that are capable of putting these particular foundational building blocks together in order to solve problems they’ve never seen before. A little bit like putting Lego pieces together. So to wrap it up, to sum up what I just said, the idea of general intelligence had a more limited meaning in cognitive science, referring to human ability to have multiple different types of skills for problem-solving and reasoning. Later on, it was also, of course, criticized in terms of the specificity of it and ignoring different kinds of intelligence. In AI, this notion has been having many different kinds of meanings. If we just mean it’s a kind of a toolbox of general kinds of intelligence for something that can be akin to an assistant to a human, that could make sense. But if we go too far and use it in the kind of absolute notion of general intelligence, as it has to encompass all kinds of intelligence possible, that might be untenable. And also perhaps we shouldn’t think about it in terms of a lump of one end-to-end system that can get all of it down. Perhaps we can think about it in terms of understanding the different components that we have also seen emerge in evolution in different species. Some of them are robust across many different species. Some of them are more specific to some species with a specific ecological niche or specific problems to solve. But I think perhaps it could be more helpful to find those cognitive and other interpersonal, cultural, different notions of intelligence; break them down into their foundational building blocks; and then see how a particular artificial intelligence agent can bring together different skills from this kind of a library of intelligence skills in order to solve problems it’s never seen before.

LLORENS: There are two concepts that jump out at me based on what you said. One is artificial general intelligence and the other is humanlike intelligence or human-level intelligence. And you’ve referenced the fact that, you know, oftentimes, we equate the two or at least it’s not clear sometimes how the two relate to each other. Certainly, human intelligence has been an important inspiration for what we’ve done—a lot of what we’ve done—in AI and, in many cases, a kind of evaluation target in terms of how we measure progress or performance. But I wonder if we could just back up a minute. Artificial general intelligence and humanlike, human-level intelligence—how do these two concepts relate to you?

MOMENNEJAD: Great question. I like that you asked to me because I think it would be different for different people. I’ve written about this, in fact. I think humanlike intelligence or human-level intelligence would require performance that is similar to humans, at least behaviorally, not just in terms of what the agent gets right, but also in terms of the kinds of mistakes and biases that the agent might have. It should look like human intelligence. For instance, humans show primacy bias, recency bias, variety of biases. And this seems like it’s unhelpful in a lot of situations. But in some situations, it helps to come with fast and frugal solutions on the go. It helps to summarize certain things or make inferences really fast that can help in human intelligence, for instance. There is analogical reasoning. That is, there are different types of intelligence that humans do. Now, if you look at what are tasks that are difficult and what are tasks that are easier for humans and compare that to a, for instance, let’s say just a large language model like GPT-4, you will see whether they find similar things simple and similar things difficult or not. When they don’t find similar things easy or difficult, I think that we should not say that this is humanlike per se, unless we mean for a specific task. Perhaps on specific sets of tasks, an agent can be, can have human-level or humanlike intelligent behavior; however, if we look overall, as long as there are particular skills that are more or less difficult for one or the other, it might be not reasonable to compare them. That being said, there are many things that some AI agent and even a [programming] language would be better [than] humans at. Does that mean that they are generally more intelligent? No, it doesn’t because there are also many things that humans are far better than AI at. The second component of this is the mechanisms by which humans do the intelligent things that we do. We are very energy efficient. With very little amount of energy consumption, we can solve very complicated problems. If you put some of us next to each other or at least give a pen and paper to one of us, this can be even a lot more effective; however, the amount of energy consumption that it takes in order for any machine to solve similar problems is a lot higher. So another difference between humanlike intelligence or biologically inspired intelligence and the kind of intelligence that is in silico is efficiency, energy efficiency in general. And finally, the amount of data that goes into current state-of-[the-art] AI versus perhaps the amount of data that a human might need to learn new tasks or acquire new skills seem to be also different. So it seems like there are a number of different approaches to comparing human and machine intelligence and deriving what are the criteria for a machine intelligence to be more humanlike. But other than the conceptual aspect of it, it’s not clear that we necessarily want something that’s entirely humanlike. Perhaps we want in some tasks and in some particular use cases for the agent to be humanlike but not in everything.

LLORENS: You mentioned some of the ways in which human intelligence is inferior or has weaknesses. You mentioned some of the weaknesses of human intelligence, like recency bias. What are some of the weaknesses of artificial intelligence, especially frontier systems today? You’ve recently published some works that have gotten into new paradigms for evaluation, and you’ve explored some of these weaknesses. And so can you tell us more about that work and about your view on this?

MOMENNEJAD: Certainly. So inspired by a very long-standing tradition of evaluating cognitive capacities—those Lego pieces that bring together intelligence that I was mentioning in humans and animals—I have conducted a number of experiments, first in humans, and built reinforcement learning models over the past more than a decade on the idea of multistep reasoning and planning. It is in the general domain of reasoning, planning, and decision making. And I particularly focused on what kind of memory representations allow brains and reinforcement learning models inspired by human brain and behavior to be able to predict the future and plan the future and reason over the past and the future seamlessly using the same representations. Inspired by the same research that goes back in tradition to Edward Tolman’s idea of cognitive maps and latent learning in the early 20th century, culminating in his very influential 1948 paper, “Cognitive maps in rats and men,” I sat down with a couple of colleagues last year—exactly this time, probably—and we worked on figuring out if we can devise similar experiments to that in order to test cognitive maps and planning and multistep reasoning abilities in large language models. So I first turned some of the experiments that I had conducted in humans and some of the experiments that were done by Edward Tolman on the topic in rodents and turned them into prompts for ChatGPT. That’s where I started, with GPT-4. The reason I did that was that I wanted to make sure that I will create some prompts that have not been in the training set. My experiments, although the papers have been published, the stimuli of the experiments were not linguistic. They were visual sequences that the human would see, and they would have to have some reinforcement learning and learn from the sequences to make inferences about relationships between different states and find what is the path that would give them optimal rewards. Very simple human reinforcement learning paradigms, however, with different kind of structures. The inspirations that I had drawn from the cognitive maps works by Edward Tolman and others was in this idea that in order for a creature, whether it’s a rodent, a human, or a machine, to be able to reason in [multiple] steps, plan, and have cognitive maps, which is simply a representation of the relational structure of the environment, in order for a creature to have these abilities or these capacities, it means that the creature needs to be sensitive and adaptive to local changes in the environment. So I designed the, sort of, the initial prompts and recruited a number of very smart and generous-with-their-time colleagues, who … we sat together and created these prompts in different domains. For instance, we also created social prompts. We also created the same kind of graph structures but for reasoning over social structures. For instance, I say, Ashley’s friends with Matt. Matt is friends with Michael. If I want to pass a message to Michael, what is the path that I can choose? Which would be, I have to tell Ashley. Ashley will tell Matt. Matt will tell Michael. This is very similar to another paradigm which was more like a maze, which would be similar to saying, there is a castle; it has 16 rooms. You enter Room 1. You open the door. It opens to Room 2. In Room 2, you open the door, and so on and so forth. So you describe, using language, the structure of a social environment or the structure of a spatial environment, and then you ask certain questions that have to do with getting from A to B in this social or spatial environment from the LLM, or you say, oh, you know, Matt and Michael don’t talk to each other anymore. So now in order to pass a message, what should I do? So I need to find a detour. Or, for instance, I say, you know, Ashley has become close to Michael now. So now I have a shortcut, so I can directly give the message to Ashley, and Ashley can directly give the message to Michael. My path to Michael is shorter now. So finding things like detours, shortcuts, or if the reward location changes, these are the kinds of changes that, inspired by my own past work and inspired by the work of Tolman and others, we implemented in all of our experiments. This led to 15 different tasks for every single graph, and we have six graphs total of different complexity levels with different graph theoretic features, and [for] each of them, we had three domains. We had a spatial domain that was with rooms that had orders like Room 1, Room 2, Room 3; a spatial domain that there was no number, there was no ordinal order to the rooms; and a social environment where it was the names of different people and so the reasoning was over social, sort of, spaces. So you can see this is a very large number of tasks. It’s 6 times 15 times 3, and each of the prompts we ran 30 times for different temperatures. Three temperatures: 0, 0.5, and 1. And for those who are not familiar with this, a temperature of a large language model determines how random it will be or how much it will stick to the first or the best option that comes to it at the last layer. And so when there are some problems that may be the first obvious answer that it finds are not good, perhaps increasing the temperature could help, or perhaps a problem that needs precision, increasing the temperature would make it worse. So based on these ideas, we also tried it for different temperatures. And we tested eight different language models like this in order to systematically evaluate their ability for this multistep reasoning and planning, and the framework that we use—we call it CogEval—and CogEval is a framework that’s not just for reasoning and multistep planning. Other tasks can be used in this framework in order to be tested, as well. And the first step of it is always to operationalize the cognitive capacity in terms of many different tasks like I just mentioned. And then the second task is designing the specific experiments with different domains like spatial and social; with different structures, like the graphs that I told you; and with different kind of repetitions and with different tasks, like the detour, shortcut, and the reward revaluation, transition revaluation, and just traversal, all the different tasks that I mentioned. And then the third step is to generate many prompts and then test them with many repetitions using different temperatures. Why is that? I think something that Sam Altman had said is relevant here, which is sometimes with some problems, you ask GPT-4 a hundred times, and one out of those hundred, it would give the correct answer. Sometimes 30 out of a hundred, it will give the correct answer. You obviously want it to give hundred out of hundred the correct answer. But we didn’t want to rely on just one try and miss the opportunity to see whether it could give the answer if you probed it again[1]. And in all of the eight large language models, we saw that none of the large language models was robust to the graph structure. Meaning, its performance got really worse as soon as the graph structure, [which] didn’t even have many nodes but just had a tree structure that was six or seven nodes, or a six- or seven-node tree was much more difficult for it to solve than a graph that had 15 nodes but had a simpler structure that was just two lines. We noted that sometimes, counterintuitively, some graph structures that you think should be easy to solve were more difficult for them. On the other hand, they were not robust to the task set. So the specific task that we tried, whether it was detour, shortcut, or it was reward revaluation or traversal, it mattered. For instance, shortcut and detour were very difficult for all of them. Another thing that we noticed was that all of them, including GPT-4, hallucinated paths that didn’t exist. For instance, there was no door between Room 12 and Room 16. They would hallucinate that there is a door, and they would give a response that includes that door. Another kind of failure mode that we observed was that they would fail to even find a one-step path. Let’s say between Room 7 and 8, there is a direct door. We would say, what is the path from 7 and 8? And they would take a longer path to go from it. And a final mode that we observed was that they would sometimes fall in loops. Even though we would directly ask them to find the shortest path, they would sometimes fall into a loop on the way to getting to their destination, which obviously you shouldn’t do if you are trying to find the shortest path. That said, there is two differing notions of accuracy here. You can have satisficing, which means you get there; you just take a longer path. And there is this notion that you cannot get there because you used some imaginary path or you did something that didn’t make sense and you, sort of, gave a nonsensical response. We had both of those kinds of issues, so we had a lot of issues with giving nonsensical answers, repeating the question that we were asking, producing gibberish. So there were numerous kinds of challenges. What we did observe was that GPT-4 was far better than the other LLMs in this regard, at least at the time that we tested it; however, this is obviously on the basis of the particular kinds of tasks that we tried. In another study, we tried Tower of Hanoi, which is also a classic cognitive science approach to [testing] planning abilities and hierarchical planning abilities. And we found that GPT-4 does between zero and 10 percent in the three-disk problem and zero percent for the four-disk problem. And that is when we started to think about having more brain-inspired solutions to improve that approach. But I’m going to leave that for next.

LLORENS: So it sounds like a very extensive set of experiments across many different tasks and with many different leading AI models, and you’ve uncovered a lack of robustness across some of these different tasks. One curiosity that I have here is how would you assess the relative difficulty of these particular tasks for human beings? Would all of these be relatively easy for a person to do or not so much?

MOMENNEJAD: Great question. So I have conducted some of these experiments already and have published them before. Humans do not perform symmetrically on all these tasks, for sure; however, for instance, Tower of Hanoi is a problem that we know humans can solve. People might have seen this. It’s three little rods that are … usually, it’s a wooden structure, so you have a physical version of it, or you can have a virtual version of it, and there are different disks with different colors and sizes. There are some rules. You cannot put certain disks on top of others. So there is a particular order in which you can stack the disks. Usually what happens is that all the disks are on one side—and when I say a three-disk problem, it means you have three total disks. And there is usually a target solution that you are shown, and you’re told to get there in a particular number of moves or in a minimum number of moves without violating the rules. So in this case, the rules would be that you wouldn’t put certain disks on top of others. And based on that, you’re expected to solve the problem. And the performance of GPT-4 on Tower of Hanoi three disk is between 0 to 10 percent and on Tower of Hanoi four disks is zero percent—zero shot. With the help, it can get better. With some support, it gets better. So in this regard, it seems like Tower of Hanoi is extremely difficult for GPT-4. It doesn’t seem as difficult as it is for GPT-4 for humans. It seems for some reason, that it couldn’t even improve itself when we explained the problem even further to it and explain to it what it did wrong. Sometimes—if people want to try it out, they should—sometimes, it would argue back and say, “No, you’re wrong. I did this right.” Which was a very interesting moment for us with ChatGPT. That was the experience that we had for trying it out first without giving it, sort of, more support than that, but I can tell you what we did next, but I want to make sure that we cover your other questions. But just to wrap this part up, inspired by tasks that have been used for evaluation of cognitive capacities such as multistep reasoning and planning in humans, it is possible to evaluate cognitive capacities and skills such as multistep reasoning and planning also in large language models. And I think that’s the takeaway from this particular study and from this general cognitive science–inspired approach. And I would like to say also it is not just human tasks that are useful. Tolman’s tasks were done in rodents. A lot of people have done experiments in fruit flies, in C. elegans, in worms, in various kinds of other species that are very relevant to testing, as well. So I think there is a general possibility of testing particular intelligence skills, evaluating it, inspired by experiments and evaluation methods for humans and other biological species.

LLORENS: Let’s explore the way forward for AI from your perspective. You know, as you’ve described your recent works, it’s clear that you have, that your work is deeply informed by insights from cognitive science, insights from neuroscience, and recent works—your recent works—have called for the development, for example, of a prefrontal cortex for AI, and I understand this to be the part of the brain that facilitates executive function. How does, how does this relate to the, you know, extending the capabilities of AI, a prefrontal cortex for AI?

MOMENNEJAD: Thank you for that question. So let me start by reiterating something I said earlier, which is the brain didn’t evolve in a lump. There were different components of brains and nervous systems and neurons that evolved at different evolutionary scales. There are some parts of the brain that appear in many different species, so they’re robust across many species. And there are some parts of the brain that appear in some species that had some particular needs, some particular problems they were facing, or some ecological niche. What is, however, in common in many of them is that there seems to be some kind of a modular or multicomponent aspect to what we call higher cognitive function or what we call executive function. And so the kinds of animals that we ascribe some form of executive function of sorts to seem to have brains that have parts or modules that do different things. It doesn’t mean that they only do that. It’s not a very extreme Fodorian view of modularity. But it is the view that, broadly speaking, when, for instance, we observe patients that have damage to a particular part of their prefrontal cortex, it could be that they perform the same on an IQ test, but they have problems holding their relationship or their jobs. So there are different parts of the brain that selective damage to those areas, because of accidents or coma or such, it seems to impair specific cognitive capacities. So this is what very much inspired me. I have been investigating the prefrontal cortex for, I guess, 17 years now, [LAUGHS] which is a scary number to say. But been … basically since I started my PhD and even during my master’s thesis, I have been focused on the role of the prefrontal cortex in our ability for long-term reasoning and planning in not just this moment—long-term, open-ended reasoning and planning. Inspired by this work, I thought, OK, if I want to improve GPT-4’s performance on, let’s say, Tower of Hanoi, can we get inspired by this kind of multiple roles that different parts of the brain play in executive function, specifically different parts of the neocortex and specifically different parts of the prefrontal cortex, part of the neocortex, in humans? Can we get inspired by some of these main roles that I have studied before and ask GPT-4 to play the role of those different parts and solve different parts of the planning and reasoning problem—the multistep planning and reasoning problem—using these roles and particular rules of how to iterate over them. For instance, there is a part of the brain called anterior cingulate cortex. Among other things, it seems to be involved in monitoring for errors and signaling when there is a need to exercise more control or move from what people like to call a faster way of thinking to a slower way of thinking to solve a particular problem. And there is … so let’s call this the cognitive function of this part. Let’s call it the monitor. This is a part of the brain that monitors for when there is a need for exercising more control or changing something because there is an error maybe. There is another part of the brain and the frontal lobe that is the, for instance, dorsolateral prefrontal cortex; that one is involved in working memory and coming up with, like, simpler plans to execute. Then there is the ventromedial prefrontal cortex that is involved in the value of states and predicting what is the next state and integrating it with information from other parts of the brain to figure out what is the value. So you put all of these things together, you can basically write different algorithms that have these different components talking to each other. And we have in that paper also, written in a pseudocode style, the different algorithms that are basically akin to a tree search, in fact. So there is a part of the role … they’re part of the multicomponent or multi-agent realization of a prefrontal cortex-like GPT-4 solution. One part of it would propose a plan. The monitor would say, thanks for that; let me pass it on to the part that is evaluating what is the outcome of this and what’s the value of that, and get back to you. It evaluates there and comes back and says, you know, this is not a good plan; give me another one. And in this iteration, sometimes it takes 10 iterations; sometimes it takes 20 iterations. This kind of council of different types of roles, they come up with a solution that is solving the Tower of Hanoi problem. And we managed to bring the performance from 0 to 10 [percent] in GPT-4 to, I think, about 70—70 percent—in Tower of Hanoi three disks, and OOD, or out-of-distribution generalization, without giving any examples of a four disk, it could generalize to above 20 percent in four-disk problems. Another impressive thing that happened here—and we tested it on the CogEval and the planning tasks from the other experiment, too—was that it brought all of the, sort of, hallucinations from about 20 to 30 percent—in some cases, much higher percentages—to zero percent. So we had slow thinking; we had 30 iterations, so it took a lot longer. And if this is, you know, fast and slow thinking, this is very slow thinking. However, we had no hallucinations anymore. And hallucination in Tower of Hanoi would be making a move that is impossible. For instance, putting in a, kind of, a disk on top of another that you cannot do because you violate a rule or taking out a middle disk that you cannot pull out actually. So those would be the kinds of hallucinations in Tower of Hanoi. All of those also went to zero. And so that is one thing that we have done already, which I have been very excited about.

LLORENS: So you painted a pretty interesting—fascinating, really—picture of a multi-agent framework where different instances of an advanced model like GPT-4 would be prompted to play the roles of different parts of the brain and, kind of, work together. And so my question is a pragmatic one. How do you prompt GPT-4 to play the role of a specific part of the human brain? What does that prompt look like?

MOMENNEJAD: Great question. I can actually, well, we have all of that at the end of our paper, so I can even read some of them if that was of interest. But just a quick response to that is you can basically describe the function that you want the LLM—in this case GPT-4—to play. You can write that in simple language. You don’t have to tell it that this is inspired by the brain. It is completely sufficient to just basically provide certain sets of rules in order for it, in order to be able to do that.[2] For instance, after you provide the problem, sort of, description … let me see if I can actually read some part of this for you. For instance, you give it a problem, and you say, consider this problem. Rule 1: you can only move a number if it’s at this and that. You clarify the rules. Here are examples. Here are proposed moves. And then you say, for instance, your role is to find whether this particular number generated as a solution is accurate. In order to do that, you can call on this other function, which is the predictor and evaluator that says, OK, if I do this, what state do I end up in, and what is the value of that state? And you get that information, and then based on that information, you decide whether the proposed move for this problem is a good move or not. If it is, then you pass a message that says, all right, give me the next step of the plan. If it’s not, then you say, OK, this is not a good plan; propose another plan. And then the part of, the part that plays the role of, hey, here is the problem. Here are the rules. Propose the first towards the subgoal or find the subgoal towards this and propose the next step. And that one receives this feedback from the monitor. And monitor has asked the predictor and evaluator, hey, what happens if I do these things and what would be the value of that in order to say, hey, this is not a great idea. So in a way this becomes a very simple prefrontal cortex–inspired multi-agent system. All of them are within the same … sort of, different calls to GPT-4 but the same instance. Just, like, because we were calling it in a code, it’s just, you just call, it’s called multiple times and each time with this kind of a very simple in-context learning text that, in text, it describes, hey, here’s the kind of problem you’re going to see. Here’s the role I want you to play. And here is what other kind of rules you need to call in order to play your role here. And then it’s up to the LLM to decide how many times it’s going to call which components in order to solve the problem. We don’t decide. We can only decide, hey, cap it at 10 times, for instance, or cap it at 30 iterations and then see how it performs.

LLORENS: So, Ida, what’s next for you and your research?

MOMENNEJAD: Thank you for that. I have always been interested in understanding minds and making minds, and this has been something that I’ve wanted to do since I was a teenager. And I think that my approaches in cognitive neuroscience have really helped me to understand minds to the extent that is possible. And my understanding of how to make minds comes from basically the work that I’ve done in AI and computer science since my undergrad. What I would be interested in is—and I have learned over the years that you cannot think about the mind in general when you are trying to isolate some components and building them—is that my interest is very much in reasoning and multistep planning, especially in complex problems and very long-term problems and how they relate to memory, how the past and the future relate to one another. And so something that I would be very interested in is making more efficient types of multi-agent brain-inspired AI but also to train smaller large language models, perhaps using the process of reasoning in order to improve their reasoning abilities. Because it’s one thing to train on outcome and outcome can be inputs and outputs, and that’s the most of the training data that LLMs receive. But it’s an entirely different approach to teach the process and probe them on different parts of the process as opposed to just the input and output. So I wonder whether with that kind of an approach, which would require generating a lot of synthetic data that relates to different types of reasoning skills, whether it’s possible to teach LLMs reasoning skills, and by reasoning skills, I mean very clearly operationalized—similar to the CogEval approach—operationalized, very well-researched, specific cognitive constructs that have construct validity and then operationalizing them in terms of many tasks. And something that’s important to me is a very important idea and a part of intelligence that maybe I didn’t highlight enough in the first part is being able to transfer to tasks that they have never seen before, and they can piece together different intelligence skills or reasoning skills in order to solve them. Another thing that I have done and I will continue to do is collective intelligence. So we talked about multi-agent systems, that they are playing the roles of different parts inside one brain. But I’ve also done experiments with multiple humans and how different structures of human communication leads to better memory or problem-solving. Humans, also, we invent things; we innovate things in cultural accumulation, which requires [building] on a lot of … some people do something, I take that outcome, take another outcome, put them together, make something. Someone takes my approach and adds something to it; makes something else. So this kind of cultural accumulation, we have done some work on that with deep reinforcement learning models that share their replay buffer as a way of sharing skill with each other; however, as humans become a lot more accustomed to using LLMs and other generative AI, basically generative AI would start participating in this kind of cultural accumulation. So the notion of collective cognition, collective intelligence, and collective memory will now have to incorporate the idea of generative AI being a part of it. And so I’m also interested in different approaches to modeling that, understanding that, optimizing that, identifying in what ways it’s better.[3] We have found both in humans and in deep reinforcement learning agents, for instance, that particular structures of communication that are actually not the most energy-consuming one; it’s not all-to-all communication, but particular partially connected structures are better for innovation than others. And some other structures might be better for memory or collective memory converging with each other.[4] So I think it would be very interesting—the same way that we are looking at what kind of components talk to each other in one brain to solve certain problems—to think about what kind of structures or roles can interact with each other, in what shape and in what frequency of communication, in order to solve larger, sort of, cultural accumulation problems.

LLORENS: Well, that’s a compelling vision. I really look forward to seeing how far you and the team can take it. And thanks for a fascinating discussion.

MOMENNEJAD: Thank you so much.

Transcript

GRETCHEN HUIZINGA: Welcome to Abstracts, a Microsoft Research Podcast that puts the spotlight on world-class research in brief. I’m Dr. Gretchen Huizinga. In this series, members of the research community at Microsoft give us a quick snapshot—or a podcast abstract—of their new and noteworthy papers. 

Today, I’m talking to Dr. Chang Liu, a senior researcher from Microsoft Research AI4Science. Dr. Liu is coauthor of a paper called “Overcoming the barrier of orbital-free density functional theory for molecular systems using deep learning.” Chang Liu, thanks for joining us on Abstracts! 

CHANG LIU: Thank you. Thank you for this opportunity to share our work. 

HUIZINGA: So in a few sentences, tell us about the issue or problem your paper addresses and why people should care about this research. 

LIU: Sure. Since this is an AI4Science work, let’s start from this perspective. About science, people always want to understand the properties of matters, such as why some substances can cure disease and why some materials are heavy or conductive. For a very long period of time, these properties can only be studied by observation and experiments, and the outcome will just look like magic to us. If we can understand the underlying mechanism and calculate these properties on our computer, then we can do the magic ourselves, and it can, hence, accelerate industries like medicine development and material discovery. Our work aims to develop a method that handles the most fundamental part of such property calculation and with better accuracy and efficiency. If you zoom into the problem, properties of matters are determined by the properties of molecules that constitute the matter. For example, the energy of a molecule is an important property. It determines which structure it mostly takes, and the structure indicates whether it can bind to a disease-related biomolecule. You may know that molecules consist of atoms, and atoms consist of nuclei and electrons, so properties of a molecule are the result of the interaction among the nuclei and the electrons in the molecule. The nuclei can be treated as classical particles, but electrons exhibit significant quantum effect. You can imagine this like electrons move so fast that they appear like cloud or mist spreading over the space. To calculate the properties of the molecule, you need to first solve the electronic structure—that is, how the electrons spread over this space. This is governed by an equation that is hard to solve. The target of our research is hence to develop a method that solves the electronic structure more accurately and more efficiently so that properties of molecules can be calculated in a higher level of accuracy and efficiency that leads to better ways to solve the industrial problems. 

HUIZINGA: Well, most research owes a debt to work that went before but also moves the science forward. So how does your approach build on and/or differ from related research in this field? 

LIU: Yes, there are indeed quite a few methods that can solve the electronic structure, but they show a harsh tradeoff between accuracy and efficiency. Currently, density functional theory, often called DFT, achieves a preferred balance for most cases and is perhaps the most popular choice. But DFT still requires a considerable cost for large molecular systems. It has a cubic cost scaling. We hope to develop a method that scales with a milder cost increase. We noted an alternative type of method called orbital-free DFT, or called OFDFT, which has a lower order of cost scaling. But existing OFDFT methods cannot achieve satisfying accuracy on molecules. So our work leverages deep learning to achieve an accurate OFDFT method. The method can achieve the same level of accuracy as conventional DFT; meanwhile, it inherits the cost scaling of OFDFT, hence is more efficient than the conventional DFT. 

HUIZINGA: OK, so we’re moving acronyms from DFT to OFDFT, and you’ve got an acronym that goes M-OFDFT. What does that stand for? 

LIU: The M represents molecules, since it is especially hard for classical or existing OFDFT to achieve a good accuracy on molecules. So our development tackles that challenge. 

HUIZINGA: Great. And I’m eager to hear about your methodology and your findings. So let’s go there. Tell us a bit about how you conducted this research and what your methodology was. 

LIU: Yeah. Regarding methodology, let me delve into a bit into some details. We follow the formulation of OFDFT, which solves the electronic structure by optimizing the electron density, where the optimization objective is to minimize the electronic energy. The challenge in OFDFT is, part of the electronic energy, specifically the kinetic energy, is hard to calculate accurately, especially for molecular systems. Existing computation formulas are based on approximate physical models, but the approximation accuracy is not satisfying. Our method uses a deep learning model to calculate the kinetic energy. We train the model on labeled data, and by the powerful learning ability, the model can give a more accurate result. This is the general idea, but there are many technical challenges. For example, since the model is used as an optimization objective, it needs to capture the overall landscape of the function. The model cannot recover the landscape if only one labeled data point is provided. For this, we made a theoretical analysis on the data generation method and found a way to generate multiple labeled data points for each molecular structure. Moreover, we can also calculate a gradient label for each data point, which provides the slope information on the landscape. Another challenge is that the kinetic energy has a strong non-local effect, meaning that the model needs to account for the interaction between any pair of spots in space. This incurs a significant cost if using the conventional way to represent density—that is, to using a grid. For this challenge, we choose to expand the density function on a set of basis functions and use the expansion coefficients to represent the density. The benefit is that it greatly reduces the representation dimension, which in turn reduces the cost for non-local calculation. These two examples are also the differences from other deep learning OFDFT works. There are more technical designs, and you may check them in the paper. 

HUIZINGA: So talk about your findings. After you completed and analyzed what you did, what were your major takeaways or findings? 

LIU: Yeah, let’s dive into the details, into the empirical findings. We find that our deep learning OFDFT, abbreviated as M-OFDFT, is much more accurate than existing OFDFT methods with tens to hundreds times lower error and achieves the same level of accuracy as the conventional DFT. 

HUIZINGA: Wow … 

LIU: On the other hand, the speed is indeed improved over conventional DFT. For example, on a protein molecule with more than 700 atoms, our method achieves nearly 30 times speedup. The empirical cost scaling is lower than quadratic and is one order less than that of conventional DFT. So the speed advantage would be more significant on larger molecules. I’d also like to mention an interesting observation. Since our method is based on deep learning, a natural question is, how accurate would the method be if applied to much larger molecules than those used for training the deep learning model? This is the generalization challenge and is one of the major challenges of deep learning method for molecular science applications. We investigated this question in our method and found that the error increases slower than linearly with molecular size. Although this is not perfect since the error is still increasing, but it is better than using the same model to predict the property directly, which shows an error that increases faster than linearly. This somehow shows the benefits of leveraging the OFDFT framework for using a deep learning method to solve molecular tasks. 

HUIZINGA: Well, let’s talk about real-world impact for a second. You’ve got this research going on in the lab, so to speak. How does it impact real-life situations? Who does this work help the most and how? 

LIU: Since our method achieves the same level of accuracy as conventional DFT but runs faster, it could accelerate molecular property calculation and molecular dynamic simulation especially for large molecules; hence, it has the potential to accelerate solving problems such as medicine development and material discovery. Our method also shows that AI techniques can create new opportunities for other electronic structure formulations, which could inspire more methods to break the long-standing tradeoff between accuracy and efficiency in this field. 

HUIZINGA: So if there was one thing you wanted our listeners to take away, just one little nugget from your research, what would that be? 

LIU: If only for one thing, that would be we develop the method that solves molecular properties more accurately and efficiently than the current portfolio of available methods. 

HUIZINGA: So finally, Chang, what are the big unanswered questions and unsolved problems that remain in this field, and what’s next on your research agenda? 

LIU: Yeah, sure. There indeed remains problems and challenges. One remaining challenge mentioned above is the generalization to molecules much larger than those in training. Although the OFDFT method is better than directly predicting properties, there is still room to improve. One possibility is to consider the success of large language models by including more abundant data and more diverse data in training and using a large model to digest all the data. This can be costly, but it may give us a surprise. And another way we may consider is to incorporate mathematical structures of the learning target functional into the model, such as convexity, lower and upper bounds, and some invariance. And such structures could regularize the model when applied to larger systems than it has seen during training. So we have actually incorporated some such structures into the model, for example, the geometric invariance, but other mathematical properties are nontrivial to incorporate. We made some discussions in the paper, and we’ll engage working on that direction in the future. The ultimate goal underlying this technical development is to build a computational method that is fast and accurate universally so that we can simulate the molecular world of any kind. 

HUIZINGA: Well, Chang Liu, thanks for joining us today, and to our listeners, thanks for tuning in. If you want to read this paper, you can find a link at aka.ms/abstracts. You can also read it on arXiv, or you can check out the March 2024 issue of Nature Computational Science. See you next time on Abstracts!