Posts in category: Microsoft AI

Transcript

ASHLEY LLORENS: I’m Ashley Llorens with Microsoft Research. I’ve spent the last 20 years working in AI and machine learning, but I’ve never felt more fortunate to work in the field than at this moment. The development of increasingly powerful large-scale AI models like GPT-4 is accelerating the advancement of AI. These models and the systems they power are exhibiting surprising new abilities like reasoning, problem-solving, and translation across languages and domains. In this podcast series, I’m sharing conversations with fellow researchers about the latest developments in large 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 Sriram Rajamani, Managing Director of Microsoft Research India. For nearly 20 years, this lab has focused on interdisciplinary research, blending theory and practice and computer science with social science. Our researchers in India have made many contributions to advance AI in areas like causal reasoning, but the latest wave of powerful AI models has made a profound impact on all the lab’s work, including their approach to creating technologies for underserved communities.

All right, so, Sriram, let’s dive right in. I think it’s fairly obvious for me to say at this point that ChatGPT—and generative AI more broadly—is a worldwide phenomenon. But what’s so striking to me about this is the way that so many people around the world can pick up the technology and use it in their context, in their own way. I was on a panel discussion a few weeks ago where I saw a comedian discover in real time that GPT-4 could write jokes that are actually funny. And shortly after that, I spoke to a student who was using ChatGPT to write an application to obtain a grazing permit for cattle. You know, the work of your lab is situated in its own unique societal context. So, what I really want to know and start with here today is, like, what’s the buzz been like for you in your part of the world around this new wave of AI?

SRIRAM RAJAMANI: Yeah. First of all, Ashley, you know, thank you for having this conversation with me. You’re absolutely right that our lab is situated in a very unique context on how this technology is going to play out in, you know, this part of the world, certainly. And you might remember, Ashley, a sort of a mic drop moment that happened for Satya [Nadella] when he visited India earlier this year, in January. So one of our researchers, Pratyush Kumar—he’s also co-founder of our partner organization called AI4Bhārat—he works also with the government on a project called Bhashini, which the government endeavors to bring conversational AI to the many Indian languages that are spoken in India. And what Pratyush did was he connected some of the AI4Bhārat translation models, language translation models, together with one of the GPT models to build a bot for a farmer to engage and ask questions about the government’s agricultural programs so the farmer could speak in their own language—you know, it could be Hindi—and what the AI4Bhārat models would do is to convert the Hindi speech into text and then translate it into English. And then he taught, you know, either fine-tuned or integrated with augmented generation … I don’t … I’m not … I don’t quite remember which one … it was one of those … where he made a GPT model customized to understand the agricultural program of the government. And he chained it together with this speech recognition and translation model. And the farmer could just now talk to the system, the AI system, in Hindi and ask, you know, are they eligible for their benefits and many details. And the, and the model had a sensible conversation with him, and Satya was just really amazed by that, and he calls … he called that as the mic drop moment of his trip in India, which I think is indicative of the speed at which this disruption is impacting very positively the various parts of the world, including the Indian subcontinent.

LLORENS: You referenced the many Indian languages written and spoken. Can you just bring, bring that to life for us? How many, how many languages are we talking about?

RAJAMANI: So, I think there are at least, you know, 30 or 40, you know, main, mainstream languages. I mean, the government recognizes 22. We call them as IN22. But I would think that there are about 30-plus languages that are spoken very, very broadly, each of them with, you know, several tens of millions, hundreds of millions of speakers. And then there is a long tail of maybe a hundred more languages which are spoken by people with … in, in smaller population counts. The real … they’re also very low-resource languages like Gondi and Idu Mishmi, which are just spoken by maybe just only a million speakers or even under a million speakers who probably … those languages probably don’t have enough data resources. So, India is an amazing testbed because of this huge diversity and distribution of languages in terms of the number of speakers, the amount of available data, and, and many of these tail languages have unique, you know, sociocultural nuances. So I think in that sense, there’s a really good testbed for, you know, how conversational AI can inclusively impact the entire world.

LLORENS: And, and what’s the … you mentioned tail languages. And so maybe we mean they’re low-resource languages like you also mentioned. What’s the gap like between what languages AI is accessible in today versus the full extent of all those languages that you just described, even just for, you know, for the Indian subcontinent? 

RAJAMANI: So what is … what we’re seeing is that with IN22, the top languages, if you look at successive versions of the GPT models, for example, the performance is definitely improving. So if you just go from, you know, GPT-2 to GPT-3 to 3.5 to 4, right, you can sort of see that these models are increasingly getting capable. But still there is a gap between what these models are able to do and what custom models are able to do, particularly if you go towards languages in which there’s not enough training data. So, so people in our lab, you know, are doing very systematic work in this area. There is a benchmarking work that my colleagues are doing called MEGA, where there is systematic benchmark being done on various tasks on a matrix that consists of, you know, tasks on one axis and languages on another axis to just systematically, empirically study, you know, what these models are able to do. And also, we are able to build models to predict how much more data is needed in each of these languages in order for the performance to be comparable to, say, languages like English. What is the … what is the gap, and how much data is needed? The other thing is that it turns out that these models, they, they learn also from related languages. So if you want to improve the performance of a language, it turns out there are other languages in the world and in India that have similar characteristics, you know, syntactic and semantic characteristics, to the language that you’re thinking about. So we can also sort of recommend, you know, what distribution of data we should collect so that all the languages improve. So that’s the kind of work that we’re doing.

LLORENS: Yeah, it’s one of the most fascinating parts of all of this—how diversity in the training dataset improves, you know, across the board, like even the addition of code, for example, in addition to language, and now we’re even seeing even other modalities. And, you know, the, the wave of AI and the unprecedented capabilities we’re seeing has significant implications for just about all of computing research. In fact, those of us in and around the field are undergoing now a process that I call, you know, reimagining computing research. And, you know, that’s a somewhat artful way to put it. But beyond the technical journey, there’s an emotional journey happening across the research community and many other communities, as well. So what has that journey been like for you and the folks at the India lab?

RAJAMANI: Yeah, that’s a good question, Ashley. You know, our work in the lab spans four areas. You know, we do work in theory and algorithms. We do work in AI and machine learning. We do systems work, and we also have an area called “Technology and Empowerment.” It’s about making sure that technology benefits people. And so far, our conversation has been about the last area. But all these four areas have been affected in a big way using this disruption. Maybe, maybe I’ll just say a few more things about the empowerment area first and then move on to the other ones. If you look at our work in the empowerment area, Ashley, right, this lab has had a track record of doing work that makes technology inclusive not just from an academic perspective, but by also deploying the work via spun-off startups, many startups, that have taken projects in the lab and scaled them to the community. Examples are Digital Green, which is an agricultural extension; 99DOTS, which is a tuberculosis medication adherence system. Karya is a, is a platform for dignified digital labor to enable underprivileged users, rural users, to contribute data and get paid for it. You know, HAMS is a system that we have built to improve road safety. You know, we’ve built a system called BlendNet that enables rural connectivity. And almost all of these, we have spun them off into startups that are … that have been funded by, you know, venture capitalists, impact investors, and we have a vibrant community of these partners that are taking the work from the lab and deploying them in the community. So the second thing that is actually happening in this area is that, as you may have heard, India is playing a pivotal role in digital public infrastructure. Advances like the Aadhaar biometric authentication system; UPI, which is a payment system—they are pervasively deployed in India, and they reach, you know, several hundreds of millions of people. And in the case of Aadhaar, more than a billion people and so on. And the world is taking note. India is now head of the G20, and many countries now want to be inspired by India and build such a digital public infrastructure in their own countries, right. And so, so, so what you saw is the mic drop moment, right? That … it actually has been coming for a long time. There has been a lot of groundwork that has been laid by our lab, by our partners, you know, such as AI4Bhārat, the people that work on digital public goods to get the technical infrastructure and our know-how to a stage where we can really build technology that benefits people, right. So, so going forward, in addition to these two major advancements, which is the building of the partner and alumni ecosystem, the digital public good infrastructure, I think AI is going to be a third and extremely important pillar that is going to enable citizen-scale digital services to reach people who may only have spoken literacy and who might speak in their own native languages and the public services can be accessible to them. 

LLORENS: So you mentioned AI4Bhārat, and I’d love for you to say a bit more about that organization and how researchers are coming together with collaborators across sectors to make some of these technology ideas real.

RAJAMANI: Yeah. So AI4Bhārat is a center in IIT Madras, which is an academic institution. It has multiple stakeholders, not just Microsoft Research, but our search technology center in India also collaborates with them. Nandan Nilekani is a prominent technologist and philanthropist. He’s behind a lot of India’s digital public infrastructure. He also, you know, funds that center significantly through his philanthropic efforts. And there are a lot of academics that have come together. And what the center does is data collection. I talked about the diversity of, you know, Indian languages. They collect various kinds of data. They also look at various applications. Like in the judicial system, in the Indian judicial system, they are thinking about, you know, how to transcribe, you know, judgments, enabling various kinds of technological applications in that context, and really actually thinking about how these kinds of AI advances can help right on top of digital public goods. So that’s actually the context in which they are working on. 

LLORENS: Digital public goods. Can you, can you describe that? What, what do we mean in this context by digital public good?

RAJAMANI: So what we mean is if you look at Indian digital public infrastructure, right, that is, as I mentioned, that is Aadhaar, which is the identity system that is now enrolled more than 1.3 billion Indians. There is actually a payment infrastructure called UPI. There are new things that are coming up, like something that’s, that’s called Beckn. There’s something called ONDC that is poised to revolutionize how e-commerce is done. So these are all, you know, sort of protocols that through private-public partnership, right, government together with think tanks have developed, that are now deployed in a big way in India. And they are now pervasively impacting education, health, and agriculture. And every area of public life is now being impacted by these digital public infrastructures. And there is a huge potential for AI and AI-enabled systems to ride on top of this digital public infrastructure to really reach people. 

LLORENS: You know, you talked about some of the, you know, the infrastructure considerations, and so what are the challenges in bringing, you know, digital technologies to, you know, to, to the Indian context? And, and you mentioned the G20 and other countries that are following the patterns. What are, what are some of the common challenges there?

RAJAMANI: So, I mean, there are many, many challenges. One of them is lack of access. You know, though India has made huge strides in lifting people out of poverty, people out there don’t have the same access to technology that you and I have. Another challenge is awareness. People just don’t know, you know, how technology can help them, right. You know, people hearing this podcast know about, you know, LinkedIn to get jobs. They know about, you know, Netflix or other streaming services to get entertainment. But there are many people out there that don’t even know that these things exist, right. So awareness is another issue. Affordability is another issue. So … many of the projects that I mentioned, what they do is actually they start not with the technology; they start with the users and their context and this situation, and what they’re trying to do and then map back. And technology is just really one of the pieces that these systems, that all of these systems that I mentioned, right … technology is just only one component. There’s a sociotechnical piece that deals with exactly these kinds of access and awareness and these kinds of issues. 

LLORENS: And we’re, we’re kind of taking a walk right now through the work of the lab. And there are some other areas that you, you want to get into, but I want to come back to this … maybe this is a good segue into the emotional journey part of the question I asked a few minutes ago. As you get into some of the, you know, the deep technical work of the lab, what were some of the first impressions of the new technologies, and what were, what were some of the first things that, you know, you and your colleagues there and our colleagues, you know, felt, you know, in observing these new capabilities?

RAJAMANI: So I, I think Peter [Lee] mentioned this very eloquently as stages of grief. And me and my colleagues, I think, went through the same thing. I mean, the … there was … we went from, you know, disbelief, saying, “Oh, wow, this is just amazing. I can’t believe this is happening” to sort of understanding what this technology can do and, over time, understanding what its limitations are and what the opportunities are as a scientist and technologist and engineering organization to really push this forward and make use of it. So that’s, I think, the stages that we went through. Maybe I can be a little bit more specific. As I mentioned, the three other areas we work on are theory in algorithms, in machine learning, and in systems. And I can sort of see … say how my colleagues are evolving, you know, their own technical and research agendas in the, in the light of the disruption. If you take our work in theory, this lab has had a track record of, you know, cracking longstanding open problems. For example, problems like the Kadison-Singer conjecture that was open for many years, many decades, was actually solved by people from the lab. Our lab has incredible experts in arithmetic and circuit complexity. They came so close to resolving the VP versus VNP conjecture, which is the arithmetic analog of the P versus NP problem. So we have incredible people working on, working on theoretical computer science, and a lot of them are now shifting their attention to understanding these large language models, right. Instead of understanding just arithmetic circuits, you know, people like Neeraj Kayal and Ankit Garg are now thinking about mathematically what does it take to understand transformers, how do we understand … how might we evolve these models or training data so that these models improve even further in performance in their capabilities and so on. So that’s actually a journey that the theory people are going through, you know, bringing their brainpower to bear on understanding these models foundationally. Because as you know, currently our understanding of these foundation models is largely empirical. We don’t have a deep scientific understanding of them. So that’s the opportunity that the, that the theoreticians see in this space. If you look at our machine learning work, you know, that actually is going through a huge disruption. I remember now one of the things that we do in this lab is work on causal ML … Amit Sharma, together with Emre Kiciman and other colleagues working on causal machine learning. And I heard a very wonderful podcast that you hosted them some time ago. Maybe you can say a little bit about what, what you heard from them, and then I can pick up back and then connect that with the rest of the lab. 

LLORENS: Sure. Well, it’s … you know, I think the, the common knowledge … there’s, there’s so many, there’s so many things about machine learning over the last few decades that have become kind of common knowledge and conventional wisdom. And one of those things is that, you know, correlation is not causation and that, you know, you know, learned models don’t, you know, generally don’t do causal reasoning. And so we, you know, we’ve had very specialized tools created to do the kind of causal reasoning that Amit and Emre do. And it was interesting. I asked them some of the same questions I’m asking you now, you know, about the journey and the initial skepticism. But it has been really interesting to see how they’re moving forward. They recently published a position paper on arXiv where they conducted some pretty compelling experiments, in some cases, showing something like, you know, causal reasoning, you know, being, being exhibited, or at least I’ll say convincing performance on causal reasoning tasks. 

RAJAMANI: Yeah, absolutely.

LLORENS: Yeah, go ahead.

RAJAMANI: Yeah, yeah, yeah, absolutely. So, so, so, you know, I would say that their journey was that initially they realized that … of course, they build specialized causal reasoning tools like DoWhy, which they’ve been building for many years. And one of the things they realized was that, “Oh, some of the things that DoWhy can do with sophisticated causal reasoning these large language models were just able to do out of the box.” And that was sort of stunning for them, right. And so the question then becomes, you know, does specific vertical research in causal reasoning is even needed, right. So that’s actually the shock and the awe and the emotional journey that these people went through. But actually, after the initial shock faded, they realized that there is actually [a] “better together” story that is emerging in the sense that, you know, once you understand the details, what they realized was that natural language contains a lot of causal information. Like if you just look at the literature, the literature has many things like, you know, A causes B and if there is, if there is, you know, hot weather, then ice cream sales go up. You know, this information is present in the literature. So if you look at tools like DoWhy, what they do is that in order to provide causal machine learning, they need assumptions from the user on what the causal model is. They need assumptions about what the causal graph is, what is the user’s assumptions about which variables depend on which variables, right? And then … and, and, and what they’ve realized is that models like GPT-4 can now provide this information. Previously, only humans were able to provide this information. And … but in addition to that, right, tools like DoWhy are still needed to confirm or refute these assumptions, statistically, using data. So this division of labor between getting assumptions from either a human or from a large language model and then using the mathematics of DoWhy to confirm or refute the assumptions now is emerging as a real advance in the way we do causal reasoning, right? So I think that’s actually what I heard in your podcast, and that’s indicative of actually what the rest of my colleagues are going through. You know, moving from first thinking about, “Oh, GPT-4 is like a threat, you know, in the sense that it really obviates my research area” to understand, “Oh, no, no. It’s really a friend. It, it really helps me do, you know, some of the things that required primarily human intervention. And if I combine GPT or these large language models together with, you know, domain specific research, we can actually go after bigger problems that we didn’t even dare going after before.” 

LLORENS: Mmm. Let me, let me ask you … I’m going to, I’m going to pivot here in a moment, but did you … have you covered, you know, the areas of research in the lab that you wanted to walk through?

RAJAMANI: Yeah, yeah, there’s, there’s more. You know, thank you for reminding me. Even in the machine learning area, there is another work direction that we have called extreme classification, which is about building very, very … classifiers with a large number of labels, you know, hundreds of millions and billions of labels. And, you know, these people are also benefiting from large language encoders. You know, they have come up with clever ways of taking these language encoders that are built using self-supervised learning together with supervised signals from things like, you know, clicks and logs from search engines and so on to improve performance of classifiers. Another work that we’ve been doing is called DiskANN, or approximate nearest neighbor search. As you know, Ashley, in this era of deep learning, retrieval works by converting everything in the world, you know, be it a document, be it an image, you know, be it an audio or video file, everything into an embedding, and relevance … relevant retrieval is done by nearest neighbor search in a geometric space. And our lab has been doing … I mean, we have probably the most scalable vector index that has been built. And, and, and these people are positively impacted by these large language models because, you know, as you know, retrieval augmented generation is one of the most common design patterns in making these large language models work for applications. And so their work is becoming increasingly relevant, and they are being placed huge demands on, you know, pushing the scale and the functionality of the nearest neighbor retrieval API to do things like, oh, can I actually add predicates, can I add streaming queries, and so on. So they are just getting stretched with more demand, you know, for their work. You know, if you look at our systems work, which is the last area that I want to cover, you know, we have, we have been doing work on using GPUs and managing GPU resources for training as well as inference. And this area is also going through a lot of disruption. And prior to these large language models, these people were looking at relatively smaller models, you know, maybe not, you know, hundreds of billions to trillions of parameters. But, but, you know, maybe hundreds of millions and so on. And they invented several techniques to share a GPU cluster among training jobs. So the disruption that they had was all these models are so large that nobody is actually sharing clusters for them. But it turned out that some of the techniques that they invented to deal with, you know, migration of jobs and so on are now used for failure recovery in very, very large models. So it turns out that, you know, at the beginning it seems like, “Oh, my work is not relevant anymore,” but once you get into the details, you find that there are actually still many important problems. And the insights you have from solving problems for smaller models can now carry over to the larger ones. And one other area I would say is the area of, you know, programming. You know, I myself work in this area. We have been doing … combining machine learning together with program analysis to build a new generation of programing tools. And the disruption that I personally faced was that the custom models that I was building were no longer relevant; they’re, they’re not even needed. So that was a disruption. But actually, what me and my colleagues went through was that, “OK, that is true, but we can now go after problems that we didn’t dare to go before.” Like, for example, you know, we can now see that, you know, copilot and so on let you give recommendations in the context of the particular file that you are editing. But can we now edit an entire repository which might contain, you know, millions of files with hundreds of millions of code? Can I just say, let’s take, for example, the whole of the Xbox code base or the Windows code base, and in the whole code base, I want to do this refactoring, or I want to, you know, migrate this package from … migrate this code base from using now this serialization package to that serialization package. Can we just do that, right? I think we wouldn’t even dare going after such a problem two years ago. But now with large language models, we are thinking, can we do that? And large language models cannot do this right now because, you know, whatever context size you have, you can’t have 100-million-line code as a context to a large language model. And so this requires, you know, combining program analysis with these techniques. That’s as an example. And actually, furthermore, there are, you know, many things that we are doing that are not quite affected by large language models. You know, for example, Ashley, you know about the HyWay project, where we’re thinking about technology to make hybrid work work better. And, you know, we are doing work on using GPUs and accelerators for, you know, database systems and so on. And we do networking work. We do a low-earth orbit satellite work for connectivity and so on. And those we are doubling down, you know, though, they have nothing to do with large language models because those are problems that are important. So, I think, you know, to summarize, I would say that, you know, most of us have gone through a journey from, you know, shock and awe to sort of somewhat of an insecurity, saying is my work even relevant, to sort of understanding, oh, these things are really aides for us. These are not threats for us. These are really aides, and we can use them to solve problems that we didn’t even dream of before. That’s the journey I think my colleagues have gone through.

LLORENS: I want to, I want to step into two of the concepts that you just laid out, maybe just to get into some of the intuitions as to what problem is being solved and how generative AI is sort of changing the way that those, those problems are solved. So the first one is extreme classification. I think, you know, a flagship use of generative AI and foundation models is, is Bing chat. And so I think this idea of, of internet search as a, as a, you know, as a, a home for, for these new technologies is, is in the popular imagination now. And I know that extreme classification seeks to solve some challenges related to search and information retrieval. But what is the challenge problem there? What, you know … how is extreme classification addressing that, and how is that, you know, being done differently now? 

RAJAMANI: So as I mentioned, where my colleagues have already made a lot of progress is in combining language encoders with extreme classifiers to do retrieval. So there are these models called NLR. Like, for example, there’s a tooling NLR model, which is a large language model which does representation, right. It actually represents, you know, keywords, keyword phrases, documents, and so on in the encodings, you know, based on, you know, self-supervised learning. But it is a very important problem to combine the knowledge that these large language models have, you know, from understanding a text. We have to combine that with supervised signals that we have from click logs. Because we have search engine click logs, we know, you know, for example, when somebody searches for this information and we show these results, what users click on. That’s supervised signals, and we have that in huge amounts. And what our researchers have done is they have figured out how to combine these encoders together with the supervised signals from click logs in order to improve both the quality and cost of retrieval, right. And, Ashley, as you said, retrieval is an extremely important part of experiences like Bing chat and retrieval augmented generation is what prevents hallucination and grounds these large language models with appropriate information retrieved and presented so that the, the relevant results are grounded without hallucination, right. Now, the new challenge that this team is now facing is, OK, that’s so far so good as far as retrieval is concerned, right? But can we do similar things with generation, right? Can we now combine these NLG models, which are these generative models, together with supervised signals, so that even generation can actually be guided in this manner, improved in both performance, as well as accuracy. And that is an example of a challenging problem that the team is going after.

LLORENS: Now let’s do the same thing with programming, and maybe I’m going to engage you on a slightly higher level of abstraction than the deep work you’re doing. And then, we can, we can, we can get back down into the work. But one of the things … one of, one of the, one of the popular ideas about these new foundation models is that you can … effectively through interacting with them, you’re sort of programming them in natural language. How does that concept sit with you as someone who, you know, is an expert in programming languages? What do you, what do you think, what do you think when someone says, you know, sort of programming the, you know, the system in natural language?

RAJAMANI: Yeah, so I, I find it fascinating and, you know, for one, you know, can we … an important topic in programming language research has been always that can we get end users or, you know, people who are nonprogrammers to program. I think that has been a longstanding open problem. And if you look at the programming language community, right, the programming language community has been able to solve it only in, in narrow domains. You know, for example, Excel has Flash Fill, where, through examples, you know, people can program Excel macros and so on. But those are not as general as these kinds of, you know, LLM-based models, right. And, and it is for the whole community, not just me, right. It was stunning when users can just describe in natural language what program they want to write and these models emit in a Python or Java or C# code. But there is a gap between that capability and having programmers just program in natural language, right. Like, you know, the obvious one is … and I can sort of say, you know, write me Python code to do this or that, and it can generate Python code, and I could run it. And if that works, then that’s a happy path. But if it doesn’t work, what am I supposed to do if I don’t know Python? What am I supposed to do, right? I still have to now break that abstraction boundary of natural language and go down into Python and debug Python. So one of the opportunities that I see is then can we build representations that are also in natural language, but that sort of describe, you know, what the application the user is trying to build and enable nonprogrammers—could be lawyers, could be accountants, could be doctors—to engage with a system purely in natural language and the system should talk back to you, saying, “Oh, so far this is what I’ve understood. This is the kind of program that I am writing,” without the user having to break that natural language abstraction boundary and going and having to go and understand Python, right? I think this is a huge opportunity in programming languages to see whether … can we build, like, for example, right, Ashley, right, I’m a programmer, and one of the things I love about programming is that I can write code. I can run it, see what it produces, and if I don’t like the results, I can go change the code and rerun it. And that’s sort of the, you know, coding, evaluating … we call it the REPL loop, right. So that’s, that’s what a programmer faces, right. Can we now provide that to natural language programmers? And since …  and I want to say, “Here’s a program I want to write,” and now I want to say, “Well, I want to run this program with this input.” And if it doesn’t work, I want to say, “Oh, this is something I don’t like. I want to change this code this way,” right. So can I now provide that kind of experience to natural language programming? I think that’s a huge opportunity if you managed to pull that off.

LLORENS: And now let’s, let’s maybe return to some of the more societally oriented, you know, topics that, that you were talking about at the top of the episode in the context of, of, of programming. Because being able to program in natural language, I think, really changes, you know, who can use the technologies, who can develop technologies, what a program … what a software development team can actually be, and who, who that, who that kind of a team can consist of. So can you paint a picture? You know, what, what, what kind of opportunities for, for, you know, software development does this open up when you can sort of program in natural languages, assuming we can make the AI compatible with your language, whatever that happens to be?  

RAJAMANI: Yeah, I think there are a lot of opportunities, and maybe I’ll, I’ll, I’ll describe a few things that we’re already doing. My, my colleagues are working on a project called VeLLM, which is now a copilot assistant for societal-scale applications. And one application they are going after is education. So, you know, India, like many other countries, has made a lot of educational resources available to teachers in government schools and so on so that if a teacher wants to make a lesson plan, you know, there is enough information available for them to search, find out many videos that their colleagues have created from different parts of the country, and put them together to create a lesson plan for their class, right. But that is a very laborious process. I mean, you have information overload when you deal with it. So my colleagues are thinking about, can we now think about, in some sense, the teacher as a programmer and have the teacher talk to the VeLLM system saying, “Hey, and here is my lesson plan. Here is what I’m trying to put together in terms of what I want to teach. And I now want the AI system to collect the relevant resources that are relevant to my lesson plan and get them in my language, the language that my students speak. You know, how do I do that,” right? And all of the things that I mentioned, right, you have to now index all of the existing information using vector indices. You have to now [use] retrieval augmented generation to get the correct thing. You have to now deal with the trunk and tail languages because this teacher might be speaking in, in, in a language that is not English, right. And, and, and, and the teacher might get a response that they don’t like, right. And how do they now … but they are not a programmer, right?  How are they going to deal with it, right? So that’s actually an example. If we, if we pull this off, right, and a teacher in rural India is able to access this information in their own language and create a lesson plan which contains the best resources throughout the country, right, we would have really achieved something.

LLORENS: Yeah, you know, it’s a, it’s a hugely compelling vision. And I’m really looking forward to seeing where you and, you know, our colleagues in Microsoft Research India Lab and MSR [Microsoft Research] more broadly, you know, take all these different directions.

RAJAMANI: Thank you, Ashley. And I was very happy that I could share the work that my colleagues are doing here and, and bringing this to your audience. Thank you so much.

Transcript

JAVIER ALVAREZ: On the third iteration, we actually moved to deep learning, and we started using GPUs in the cloud.

RAJ JENA: I’m really interested in this part of the story, the “final mile” story, where you actually take something and instead of just topping out at saying, “Hey, we did something. Let’s write a paper” — which we did do! — you actually stick with it and get it all the way through to clinical impact.

ALVAREZ: So we started training models with 30 million parameters. And this was a huge breakthrough. So we started to get really good feedback from Raj and his colleagues at Addenbrooke’s. Uh, yeah, it was a great experience.

JENA: In 2016, some changes came to the team. Javi joined, and we were so excited because he was a software engineer, where before we had been researchers talking to researchers, and it was the ability to know that really good software engineering was going to be able to take something we built as research and make it good enough to plumb in the hospital as Javi described. That was a real exciting moment.

GRETCHEN HUIZINGA: You’re listening to Collaborators, a Microsoft Research Podcast showcasing the range of expertise that goes into transforming mind-blowing ideas into world-changing technologies. I’m Dr. Gretchen Huizinga.

I’m excited to be talking today with Javier Alvarez and Dr. Raj Jena. Javier is a Senior Director of Biomedical Imaging at Microsoft Health Futures in Cambridge, UK, and part of Project InnerEye, a machine learning technology designed to democratize AI for medical image analysis across the spectrum from research to practice. Raj is a radiation oncologist at Addenbrooke’s hospital, which is part of the Cambridge University Hospitals system, and he was also a collaborator with Project InnerEye during the research phase. Javier and Raj, welcome to the podcast. Now, before we peer into InnerEye, let’s get to know you a little bit better! Javier, I’ll start with you. Give us a brief overview of your training and expertise and then tell us about Microsoft Health Futures and your role there.

JAVIER ALVAREZ: Thank you for having me here. I’m Javier, and I lead the biomedical imaging team at Microsoft Health Futures. We are responsible for research, incubations, and moonshots that drive real-world impact across healthcare and life sciences inside MSR. Uh, yeah, my team is very diverse. We focus on end-to-end solutions. We collaborate with people like Raj, mostly clinicians, and we work on high-quality research, and we hope others can build on top of our work. We try to integrate our AI as a “friendly colleague.” And yeah, I have been in Microsoft for 10 years. My background is in computer science and engineering, and I have been always working on research and innovation projects, uh, focusing on high-risk/high-reward projects. And yeah, my first job at Microsoft was actually working on the first telemetry pipeline for Microsoft on, on the Azure cloud. And we helped several products like Skype, Xbox, Office, and Bing to get better insights into their data. And yeah, after that I joined Antonio Criminisi and Raj in 2016 to work on InnerEye. So yeah, I’m super, super excited to be here to share more about our work.

HUIZINGA: Well, Raj, our audience is a super smart one, but probably not all that well-versed on radiation therapy and neuro-oncology. So tell us about your work as a cancer doctor and a researcher, as well. What’s your background, and how would you define your role — or roles, plural — at Cambridge University Hospitals?

JENA: Thanks for the opportunity to join this discussion and to fly the flag for radiation oncology. It’s a really useful and very modern anti-cancer therapy. Half the people diagnosed with cancer who are cured will end up having radiation therapy as part of their treatment pathway. So I’m passionate about making radiation therapy as safe, as smart and accurate, and with as few side effects as possible. And I do that both in the context of my clinical work but also research work, where I focus mainly on sort of the analysis of images. We use an awful lot of imaging in radiation therapy to really target the radiation therapy. And it’s in that context, really, that I kind of started, you know, with this collaboration over 10 years ago now.

HUIZINGA: Wow. What would you say your “split” is? I mean, as a doctor or a researcher, how do you balance your time?

JENA: Some people would say I have the dream job because I do half and half. Half clinical work and half research work. And I really like that because it means that I can anchor myself in the clinic. I don’t lose track of why we’re trying to do these things. We’re trying to bring benefit to patients, to my patients. But it also means I’ve got the time to then explore on the research side and work with the best and brightest people, including, you know, many of the guys I’ve met at Microsoft Research.

HUIZINGA: Right. You know, as a side note, I just finished a book called The Butchering Art about Joseph Lister, who was both a surgeon, in the Victorian era, and also a researcher and sort of discovering this idea of germ theory and so on with Louis Pasteur, etc. So I’m, I’m ensconced in this idea of research and practice being so tightly woven together. So that’s really awesome. Well, before we get into specifics on the collaboration, Project InnerEye warrants a little bit of explication itself. From what you’ve described, I’d call it a “machine learning meets radiation therapy” love story, and it’s a match made in heaven, or at least the cloud. So what’s the catalyst for InnerEye, and how have the research findings changed the game? Raj, why don’t you talk about it from the medical angle?

JENA: Sure. So, um, as with many things, it started by chance. I went to a talk given by Antonio Criminisi, who Javi mentioned. He was the person that kind of established the InnerEye group at Microsoft Research back in 2011, I think. And he was talking about the way that his team, that did computer vision at the time, were using algorithms that had been developed to detect the human pose so that actually you could play video games without a controller. So this was technology that we all know and love in terms of systems like Kinect and the Xbox. You know, I had one of those! But I went to listen because Antonio wanted to apply it to medical imaging. So in the same way that they were using algorithms to mark out where the body was or where the hands were, could we also mark out tissues and structures within the body? So I said to him, after the end of this, you need to come and see what we do in radiation therapy because this really matters. And to his credit, he did! A couple of weeks later, he came to the department, and he went into a room where dozens of my colleagues were sitting in front of computers, working as fast and accurately as they could, to manually mock up all this normal anatomy on CT scans so we could get our patients onto radiotherapy as quickly as possible. And that was the light bulb moment where he realized, yeah, we need to make this better; we need to make this faster and use, initially, algorithms that came from computer vision, but now, you know, we’ve moved slowly over to things now that we would consider to be sort of machine learning and AI algorithms.

HUIZINGA: Right. Well, I should note that I’ve interviewed Antonio on this show, um, a few years back. And so if listeners want to go back to the archives and find the episode with Antonio Criminisi, that was a great one. So what you just described is sort of a “I can do this, but I can’t do it very fast” scenario. So let’s go into the geek side. Um, Javier, talk about the technical aspects of InnerEye and what it brought to the game. How has the research evolved? Where did it start, from your perspective, and where has it come in the cloud era?

ALVAREZ: Sure, yeah. I would be happy to geek out a bit! Um, so one of the biggest challenges that we faced in radiotherapy was working with CT scans. So CT scans are 3D images that contain around 20 million 3D pixels. We usually call them voxels. And we need to classify each of them as background, different organs, or tumor. And this actually requires a lot of compute and memory. So when we started in 2016, actually we started using very simple models called decision forests, and these can be trained on CPUs. So it was really easy to train them, but one of the problems with decision forests is that you actually have to do the feature extraction manually. So we had to code all that, and it’s a bit of a limitation of this approach. So in the second iteration, we started connecting the hospital to the cloud, and that gave us access to more compute, and we started introducing what we call the InnerEye-Gateway. So this actually helped to automatically route de-identified CT scans to the cloud and run the computation there. And we managed to integrate the model seamlessly into the workflow. So clinicians, when they go to open their CT scan, they already have the segmentation ready to be used on their favorite planning tool. They can review it and refine it. And then on the third iteration, we actually moved to deep learning, and we started using GPUs in the cloud. And this actually helped us create bigger models with more capacity to learn these complex tasks. So we started training models with 30 million parameters. And this was a huge breakthrough. So we started to get really good feedback from Raj and his colleagues at Addenbrooke’s. Uh, yeah, it was a great experience. We had to iterate many times and go to the hospital down the road here in Cambridge. And yeah, it wasn’t a straight path. We had to learn a lot about the radiotherapy workflow, and yeah, we actually learned that it’s actually very hard to deploy AI.

HUIZINGA: Yeah. Every time we do a podcast, um, listeners can’t see the other person shaking their head, but Raj has been shaking his head the whole time Javier’s talking. Talk a little bit, Raj, about that marriage of workflow and machine learning. How did it change your world?

JENA: Yeah, I mean, I think I’m really interested in this part of the story, the “final mile” story, where you actually take something and instead of just topping out at saying, “Hey, we did something. Let’s write a paper” — which we did do! — you actually stick with it and get it all the way through to clinical impact. And actually, you know, from my point of view, in 2016, some changes came to the team. Javi joined, and we were so excited because he was a software engineer, where before we had been researchers talking to researchers. And it was the ability to know that really good software engineering was going to be able to take something we built as research and make it good enough to plumb in the hospital as Javi described. That was a real exciting moment. And then the second exciting moment that followed from that was the first time our clinicians saw the output from that third iteration that Javi mentioned, the deep learning model, and you looked at their reactions because they’re thinking, I couldn’t immediately tell this was done by AI.

HUIZINGA: Wow!

JENA: And that was the moment I will never forget. Because they were very kind to us. They evaluated the models at the beginning, when the output wasn’t good enough and they said, hey, this is interesting, but, you know, we’re not really going to use it. It’s not really going to save us time. And they stuck with us, you know, the clinician part of the team stuck with the researcher part of the team, and we kept going. And it was that moment really when everything came together and we thought, yeah, we’re onto something. That was … that was huge.

HUIZINGA: Yeah. It sounds like you’re talking about how you met, but I’m not sure if that’s the whole story. So let’s talk about the meet-up and how the two of you, specifically as collaborators, started working together. I always like to call this “how I met your mother,” but I’m interested to hear each side of the story because there’s always an “aha moment” on what my work could contribute to this and how theirs could contribute to mine – the kind of co-learning scenario? So, Raj, go a little further in describing how Javi and you got together, and then we’ll see if Javier can confirm or deny the story! [LAUGHS]

JENA: Yeah. So as, as I mentioned … so I had already been working with Antonio purely as research for a little while, and Antonio was tremendously excited because he said the team was going to expand, and Javier was one of the first hires that we actually had to join the team. And I remember Antonio coming in and said, “We’ve just interviewed and appointed this guy. You wait till you … you wait till you meet him,” kind of thing. And then Javi joined us. From my point of view, I am a doctor that likes to code, so I like seeing code come to action, and I know the joy that that brings. And there was this amazing time, shortly after Javi first joined us, where I would come and meet the team about once a week and we would say, hey, you know, maybe we should do this and maybe this would be the way to solve this particular problem, or we need to design a tool so we can visualize the imaging and the machine learning parts of our workflow together and work on them together. And I come back next week, and the thing was practically built! And, you know, to me, that was just the amazing thing … is what you realized is that where before we had been struggling along with just researchers trying to do their best — you know, we know the maths but not how to build things — all of a sudden, Javi comes along and just the rate and the pace at which stuff move forwards, it was incredible! So yeah, that’s my side of the story.

HUIZINGA: I love it. Um, in fact, a doctor that likes to code … I’m wondering if Javier is a computer scientist that likes to … I don’t even know how to fill in the blank on your end … radiotherapy? Dabble in operation? Javier, what’s your side of the story?

ALVAREZ: Yeah, I think for me, it was really amazing to work with Raj because he was telling us about all the physics about radiotherapy, and this was super exciting. We went on multiple trips to Addenbrooke’s to see the radiotherapy department. So actually, yeah, for me, I, I … that was my first project on healthcare, so I had to learn a lot. So yeah, it was super useful to work with Raj, learning about the workflow in radiotherapy, how the data moves, as well. It was super useful. I think actually we met here with Antonio during lunch in the lab. Uhh, yeah…

HUIZINGA: During lunch in the lab … ! [LAUGHS] It would be a good time now for me to just clarify that Addenbrooke’s is the old name of the hospital that’s part of … um, Raj, explain that!

JENA: That’s right. So we’re now called Cambridge University Hospitals to reflect the fact that we’re a big biomedical campus and we actually have multiple hospitals: Addenbrooke’s, the Rosie, uh, Papworth Hospital … but affectionately, people who have lived in Cambridge still call it Addenbrooke’s.

HUIZINGA: That’s good. We can call it both. Javier, as we’re recording this podcast, some big things are going on in the UK. Um, it’s the 75th anniversary of the National Health Service, or NHS, and you guys recently got an award from that organization. You’ve written a JAMA paper and even the prime minister posted something on LinkedIn about your work, which is pretty cool! Tell us about some of the accolades associated with InnerEye right now, from where it started — you know, as a twinkle in someone’s eye — to where it is now, what kind of attention it’s getting. What’s the buzz?

ALVAREZ: Yeah, absolutely. Yeah, maybe I’ll talk about the JAMA paper, and I will let Raj talk about the NHS part, because I think this has been mostly his work.

HUIZINGA: Perfect.

ALVAREZ: So yeah, I think when we started getting really good results with our models in Addenbrooke’s and sharing it with the clinicians, we thought that yeah, we wanted to run a bigger study on evaluating the models for prostate and head and neck. Uh, so we ran a study that was published in JAMA, and here we asked the question of, OK, are these models actually acceptable and accurate enough for radiotherapy planning? And can we actually reduce the time in the workflow? So we, we actually got around eight datasets from all around the world, very diverse datasets from radiotherapy planning, and we set aside a couple of them for external validation. So we didn’t use those for training. And then we used the, the rest of them for training the model. And we actually show in the paper that the model generalizes to the external datasets, so it’s quite robust, using different protocols in radiotherapy. And we also did some interobserver variability study to check that the variability of the AI model is similar to the variability that we observed between different clinicians. And, yeah, as part of the paper, we actually open-sourced all the code. This is how Addenbrooke’s actually started to think about deploying the models clinically. Uh, yeah, in fact this work was recognized with this NHS AI Award and now with the NHS anniversary, but, yeah, I’ll let Raj talk about this part in the hospital.

HUIZINGA: Well, before we go to Raj, I want you to just clarify, because I think this is super interesting. You’ve got the paper and you’ve got practice. And what’s fascinating … I’ll say it again—I just finished the book—but what Joseph Lister did was practice and show how his theories and his work made a difference in his patients’ lives. But what you’re talking about, as you mentioned, Javier, is background, organ, tumor …

ALVAREZ: Yeah.

HUIZINGA: So those three things have to be differentiated in the radiologist’s workflow to say, I’m not going to shoot for the background or the organ; I want to get the tumor. And what you’re saying, Javier, is that this tool was able to do sort of human-level identification?

ALVAREZ: Yeah. Yeah, exactly. Yeah. This is what we, we showed in the JAMA paper. Yeah.

HUIZINGA: Well, Raj, talk about it from the medical angle. Um, what’s the buzz from your end?

JENA: Sure. Yeah. So, so InnerEye is a toolkit, and it was great to see it being used for all sorts of things, but in radiation therapy, we’re using that toolkit specifically to mark out the healthy organs that need to be shielded from radiation. At the moment, we’re not using InnerEye to try and mark out the tumor itself because tumors change a lot from person to person. And so what our design was, was to build something that very much assists rather than replacing the oncologist so that when the oncologist sits down to do this task, about 90 percent of the time is spent marking out all of the healthy organs and 10 percent of the time on the tumor. Actually, we’d love it to be the other way around. And that’s what this tool does. It means that when the oncologist sits down, all of the healthy organs that sit around the tumor that need to be shielded as much as possible from the radiation, that’s already done. So the oncologist goes through … they have to review it, obviously, and check each one is accurate. And in our real-world testing, we found out that about two times out of three, the tool does a good enough job that its output can be used directly without changing anything, which is really good.

HUIZINGA: Wow.

JENA: That means they can then focus on contouring the tumor, and it means the overall time taken to complete this task can be about two and a half times faster. Now, when you think, for the complex tumors that we deal with, that can take up to two hours, that’s a lot of time saving and that’s time given back to the oncologist to spend in front of the patient, basically. So from our point of view, Javi mentioned this, uh, NHS award—it was this AI award that we were given by our national healthcare service—and what that was charged to do was to pick up the baton, once Microsoft had turned InnerEye to an open-source tool, because to turn that open-source tool into a potential medical device that could be used in the cloud for real clinical care, needs a whole other level of sort of checks and evaluations. And that’s what we did, basically, in our team. We worked together with the team in our hospital that builds things as medical devices. Usually, in our hospital, that team builds what we call prosthetics. So things that you would put into a patient or onto a patient when they’ve been injured or something like that. They’d never done it for a software device. But it was great because we had some really strong starting points. First of all, we knew that the actual InnerEye code was fantastic, and secondly, we knew from the JAMA paper that the initial evaluations, in terms of how useful these things were, stood up very well. So that, together with our own clinical evaluations of having the tool plumbed in and seeing it being used, meant that we kind of already knew that this was going to be possible, that we were likely to succeed in this task.

HUIZINGA: Hmmm. Go back a little bit, Raj. You’ve mentioned that tumors change from patient to patient, so it’s not always the same. Do they also change over time?

JENA: Yes. Hopefully, they shrink after radiation therapy and the treatments that, that we give! And so yes, I mean, it’s a big part of what these sorts of tools will continue to be explored in the future is actually tracking how tumors change over time, and that’s a big area. But, you know, we chose to pick on something that was achievable, that wasn’t too risky, and that would already achieve real utility, you know, in, in a hospital. So we already did that with even what it does in terms of marking out the healthy organs. The tumor stuff will come, I’m sure, in time. But we already proved that you could use these tools and build them to be useful.

HUIZINGA: Right. Javier, you mentioned earlier that one of the mandates of the lab is high-risk/high-reward research. This seems to have super high reward, but it’s about now that I ask what could possibly go wrong to everybody that comes on the show. [LAUGHS] Some people hate it. Some have worried that AI will take jobs away from doctors, and I’m sure there’s other worries, as well. What thought have you given to potential consequences, intended and unintended, as you move forward with this work, and what strategies are you employing to mitigate them? Let’s hear from the technologist first, and then we’ll hear from the doctor.

ALVAREZ: Yeah, absolutely. I believe, uh, AI safety should be our top priority in any of our AI products in healthcare. And yeah, it is super important to consider the intended and unintended consequences of deploying these models into the clinical workflow. One of the top-of-mind concerns for the public is that AI might take jobs away from doctors, but actually, we need more doctors. So one out of five jobs in oncology are not being filled in the UK, and the way we are thinking about deploying these AI models is to augment the clinicians. So we want to help them be more productive and deliver better patient outcomes. So the models are working alongside the doctor. And in the case of InnerEye, we are delivering more accurate and faster segmentation. Other concerns could be biases in the models, and to mitigate this, we usually work with clinicians like Raj to build diverse and good datasets that are representative of the population. As always, we make sure the clinician has the ultimate decision and they approve the work of the AI model.

HUIZINGA: Raj, what’s your take on the “what could possibly go wrong” question?

JENA: Yeah, it’s an interesting one. You know, we’ve identified 500 risks, and we’ve gone through each and every one of them and made sure either that the software means that it can’t happen or we mitigate it, basically. Actually, though, the biggest thing that you can do to mitigate risk is talk to patients. And as part of this award, we got to do two really interesting consultations with patients, because then you understand the patient’s perspective. And two things, very briefly, that I took home from that: the first is, is that patients say, yeah, OK, this isn’t what I thought of when I think about AI. I understand that you’ve used incredibly advanced machine learning tools, but actually, this is a very simple task, and the risk is relevant to the task rather than the technology. So that was a useful thing. And the second thing is that they said, it’s all about who’s in control. I understand how this system works to assist an oncologist, and the oncologist retains ultimate control, and that is a huge thing in terms of enhancing trust. So I think as you move from these types of systems to systems where actually you start to push the envelope even further, it’s really important to take patients with you because they keep you grounded, and they will give you really good insights as to what those real risks are.

HUIZINGA: Right.

JENA: The other thing is, is that everyone knows, just like any job, you know, there are the bits that excite you and reward you. And then there are the bits that are kind of dull and tedious. And, you know, Eric Topol has this famous phrase that he said, you know, which is that good AI should give clinicians the gift of time, and that’s what you really want … is, is that you want the AI to allow you to spend more of the time that interests you, excites you, fascinates you, motivates you. And I think, you know, from my point of view, I’m a great believer that that’s what AI will do. It will actually, you know … doctors are very adaptive. They’ll learn to use new tools, whether it’s a robot from a surgeon’s point of view or a new AI algorithm, but they’ll use it in the best way possible to actually kind of still allow them to achieve that patient-centric care.

HUIZINGA: Well, that’s a lovely segue way into the next question I had for you anyway, which is what could possibly go right. And you, Raj, referred to the triple benefit of InnerEye. Go a little deeper into who this research helps and why and how.

JENA: I think it’s a really important illustration of how you can democratize AI. A lot of AI research work stays as research work, and people don’t really understand how these tools … they hear a lot about it, and they read a lot about it, but they don’t understand how it’s actually going to make a difference for them in the clinic. And I think that’s why, you know, stories like InnerEye are particularly meaningful. We’re not talking about building an AI that lets us understand something that the human couldn’t understand before. So it’s not earth shattering in that sense. And yet, even despite that simplicity, so many of my colleagues, they get it. They go, OK, you know, we really understand you’ve actually built something, and you’ve put it here into the clinic. And I think, you know, from my point of view, that’s the real value. There are other value propositions relating to the fact that it was open-source that lends itself to democratization and sharing and also because it runs in the cloud and that basically you don’t need a hospital that’s already got a quarter million-pound computer and only those hospitals with the latest kit can actually use it. So it means that it is just as easy to deploy in a small hospital as it is in a big hospital. So for me, those are the key messages, I think.

HUIZINGA: Javier, Raj just alluded to the open-source nature of this tool or toolkit. I want you to drill in a little more on that story. Um, I understand this lives on GitHub. How did that decision come about, and why do you believe this will benefit people in the future?

ALVAREZ: Yes. So the decision to make the code open-source came from the desire to democratize the access to these AI models. So we wanted to make sure everyone would be able to build on top of our research. And that was the way that we found to give access to Addenbrooke’s to create their own medical devices. We thought that also having open-source code allows us to be more transparent with our research and to gain trust on the technology. It also helps us, as well, to get help from the community on building this project. So we had people helping us to fix bugs and to make sure, uh, the algorithms are not biased. As part of the open-source, we made available three big components. One is the InnerEye-Gateway that routes the images to the AI models in the cloud and de-identifies the data. We also made available the InnerEye inference code that basically is an API that the InnerEye-Gateway uses to run the models. And also all the training code to be able to reproduce our work. Uh, yeah, we are super excited to see how people will use the open source in the future. We also have some startups that are using our code and trying to build products with it.

HUIZINGA: Go a little further, Javier, because this is interesting. Obviously, radiation therapy is one application of InnerEye, but I imagine it could be useful for other medical applications or other … actually, probably anything that you need to identify something, you know, the signal in the noise.

ALVAREZ: Yeah, um, segmentation in medical imaging is super important, so it allows you to actually strike measurements from the images. So, yeah, it can be useful, as well, in some radiology scenarios like clinical trials where you want to track tumors over time. And also in surgery where you want to plan surgery, so you need to understand how vessels are feeding into the tumor. So, yeah, segmentation is super important, and I think the components that we have could be useful for many different scenarios in medical imaging.

HUIZINGA: Well, Raj, I always like to know where the project is on the spectrum from lab to life, and as I understand it, after the InnerEye team completed the research and made the code open source, Addenbrooke’s took the regulatory baton for medical device approval in the UK, but it’s still not over. So continuing with that analogy: if this were a relay race and the idea was the first leg, who else is running, where are you in the race, and who brings it across the finish line?

JENA: Yeah, that’s a really good analogy. I, I might use that one in the future. So, uh, there are other commercial organizations that have systems that will perform this work. They are quite expensive, actually, to buy into if you want to buy them outright. There are some where, a bit like ours, you can scale it so that you pay as each patient’s data is processed. They also are quite expensive for some emerging, uh, healthcare markets, and by emerging healthcare markets, I include my own in the, in the NHS. To our knowledge, we are the only cloud-based, open-source medical imaging device that we’re actually trying to build within the NHS. So that is truly unique. And in terms of where we are on that journey to take the, you know, the InnerEye open source all the way through to a medical device that actually, you know, you can buy off the shelf and have all of the associated support and, you know, technical specifications that you need to use in practice, we’re at this point where the hospital has basically finished all of that work. The hospital has been incredibly supportive of this entire research for the last 10 years, but it can’t act as a manufacturer. It’s quite difficult to do that. So we’ll then partner with a manufacturer, actually a company that’s a friend to us in the hospital and to the InnerEye team, too, and they will be responsible for basically taking all of the work that we’ve done to prepare the medical device certification documents and then actually going through that device certification and bringing it to the market. So it’s very exciting, you know, to be literally at that final stage of the, of the story.

HUIZINGA: Right. Ready to run across the finish line. I like to end each podcast with a little vision-casting, and I’ve been shocked at how profoundly healthcare has advanced in just the last hundred and fifty years. So I won’t ask you to project a hundred and fifty years out, but if InnerEye is a truly game-changing technology, what does healthcare, and especially oncology, look like in the future, and how has your work disrupted the field and made the world a better place? Javier, why don’t you talk about it from the technical aspect, and then maybe Raj can bring the show home from the medical aspect.

ALVAREZ: Sure. Yeah. One exciting, uh, development on the horizon is the use of GPT-4 in radiology or maybe even in radiotherapy. We are also working on multimodal learning now and trying to expand the work that we have done with InnerEye to radiology, where there is a much bigger opportunity. Uh, with multimodal learning, we are trying to integrate multiple sources of data like medical images, text, audio, and also different types of modalities because we want to make sure we can use CT scans, MRI, x-rays … and yeah, this requires developing new types of models, and these models need to be able to generalize to many different tasks because we have a huge need for AI in healthcare, and the current way of, uh, building these models is we develop one model for every use case, and this is not scalable. So we need more general-purpose models that can be specialized really quickly to different needs. And I think the other thing that excites me is actually … maybe this is quite far away, but how do we create a digital copy of the human body for every person on the planet and we create some sort of digital twin that we can actually use to run simulations? And I think medical imaging is going to be a big, important part of this. And we can use that digital twin to run interventions and figure out how can we treat that patient, what is happening with that patient, so, yeah, I think it’s super exciting, the potential of AI in healthcare, but of course we need to make sure we look at the risks, as well, of using AI. But yeah, there are many positive opportunities.

HUIZINGA: Right. I’m just shaking my head and my jaw is dropped: my digital twin in the future! [LAUGHS] Raj?

JENA: I mean, I think it’s a tremendously exciting time, and we live in an exponential age where things are coming and new innovations are coming at a faster and faster rate. I think what we have to do is to really, as doctors, learn from history and adapt to make sure that we stay able to retrain and reconfigure ourselves, and reconfigure medicine, to keep up to speed with the digital technologies. You know, just to give an example to what you were talking about with Joseph Lister; it’s fascinating. You know, I always think about, you know, Semmelweis and a similar story. So he was an Austrian obstetrician who, for the audience, a hundred and fifty years ago worked out that actually if you wash your hands after delivering a baby from a mother, the mother was less likely to get a fever and less likely to die. He was 29 when he worked that out, and yet it took nearly 20 years for him to convince the medical community basically because they felt threatened. And, you know, that was the key thing. They just, you know, there wasn’t that level of understanding of, you know, that we need to think and adapt and incorporate new ideas and new thinking. And we will be challenged, you know, severely, I think, in the years to come, with new technologies. I’ve just come back from a conference talking about foundation models and GPT in medical imaging and, um, you know, there was a huge amount of excitement. One really interesting point that I heard is that these models were built on all of the images, mainly generated by cameras, on the internet and social media sphere, and if you add up all of the medical imaging that’s ever been done, it’s only about 1 percent of that image data. So it’s always going to be hard. And of course, we can’t always access all of that information, you know, for patient confidentiality and, you know, numerous factors. So it may take a little while before we have these amazing, generalizable AI models in medicine, but I’m sure they’ll come, and I think the biggest thing that we can do is to be ready for them. And the way I believe that you do that is in little steps, is to start bringing very simple, explainable, transparent AI into your workplace—of which, you know, InnerEye is a really good example—so that, you know, you can look inside the box, start to ask questions, and understand how it works because then, when the next AI comes along, or maybe the AI after that, that integrates more data than the human mind can hold together to make a decision, then you need to be comfortable with your ability to query that, to interrogate that, and make it safe, you know, for your patients. Because at the end of the day, for thousands of years, doctors have evaluated things. And yeah, I think, I think those things won’t change, you know, but we just … we’ve got to up our game, you know, so I’ve got to be as good as Javi is in kind of understanding how these things, how these things work. So …

HUIZINGA: Well, I love my job because I learn something new every show. And this one has been a humdinger, as they say. Thank you so much for taking time to educate us on InnerEye today.

ALVAREZ: Thank you.

JENA: Thanks. It’s been a pleasure.