Behind The Tech with Kevin Scott - Dr. Daniela Rus, Director of MIT Computer Science and AI Lab

🎁Amazon Prime 📖Kindle Unlimited 🎧Audible Plus 🎵Amazon Music Unlimited 🌿iHerb 💰Binance

DANIELA RUS: I believe that we can stretch ourselves and go to a different stage, where we think about soft robots that are inspired in shape by the animal kingdom with its form diversity, by the natural world, with its form diversity, and even by the built environment. Because then, we would have so much more potential for applications.

KEVIN SCOTT: Hi, everyone. Welcome to Behind the Tech. I’m your host, Kevin Scott, Chief Technology Officer for Microsoft.

In this podcast, we’re going to get behind the tech. We’ll talk with some of the people who have made our modern tech world possible and understand what motivated them to create what they did. So, join me to maybe learn a little bit about the history of computing and get a few behind-the-scenes insights into what’s happening today. Stick around.


CHRISTINA WARREN: Hello and welcome to Behind the Tech. I’m Christina Warren, senior cloud advocate at Microsoft,

KEVIN SCOTT: And I’m Kevin Scott.

CHRISTINA WARREN: And our guest on the show today is Daniela Rus. Daniela is a researcher, MIT professor and the head of MIT’s Computer Science and Artificial Intelligence Lab, better known as CSAIL. She’s a USA expert member for Global Partnership on AI. She’s a member of the Board for Scientific America and was recently recognized, along with another Behind the Tech guest, Fei-Fei Li, as one of the eight most influential women in AI.

KEVIN SCOTT: Yeah, Daniela is really impressive, as is the institution that she leads. I’ve always been an admirer of her work in AI robotics, and she’s just a tremendous computer scientist.

CHRISTINA WARREN: Yeah, no, I – I’ve always been an admirer of what happens at MIT, especially with the robotics stuff, and – and I can’t wait to hear the conversation that you two have.

KEVIN SCOTT: Yeah, me – me too.

CHRISTINA WARREN: All right, let’s chat with Daniela.


KEVIN SCOTT: Our guest today is Daniela Rus. Daniela is one of the world’s leading roboticists. She’s an MIT professor and the first female head of MIT’s Computer Science and Artificial Intelligence Lab, where her research focuses on robotics, mobile computing and data science.

Daniela is a class of 2002 MacArthur Fellow, a member of the National Academy of Engineers, and the recipient of the 2017 Engelberger Robotics Award from the Robotics Industries Association. Her work is dedicated to envisioning a future where robots are integrated into the fabric of everyday life. Welcome to the show Daniela.

DANIELA RUS: Thank you so much. I’m excited to be here.

KEVIN SCOTT: Yeah, we’re super excited to have you. And so, I would love to start our conversation today by talking a little bit about how you got interested in technology in the first place. Were you a kid when the spark got lit, or was it later?

DANIELA RUS: So, I grew up in Romania, and during the time of my childhood in Romania, there was not much activity on TV, but I watched Lost In Space with great fervor. That was one of the few shows there was on TV at that time. I also read the books of Jules Verne, and I fantasized about faraway places and about superpowers. But I was also interested in poetry, and art, and history, and math and literature, and just about anything else I could get my hands on. But the constraints of growing up in Romania and being good at math and physics put me directly on the STEM track.

DANIELA RUS: And so, at that time, it was standard practice for high school age students to spend a week each month working in a factory. The Romanian government believed this would help us get some trade skills, and it would be easier to join the proletariat this way. So, for some time, one week each month, I worked in a factory that made spare parts for locomotives, and I was a teenager at that time. This work didn’t feel very useful to me then.

But as I look back, I can now see many ways in which this experience contributed to my career journey, because I learned how to use machines, such as a lathe. I made screws from scratch, and I began to understand the physical aspects of making things.

And as the math we learned in school became more abstract, I realized that I wanted to do something related to STEM, but also something with a physical component. And, in my opinion, getting to be at MIT to work with the most extraordinary students on building new robots, designing and inventing new machines is really living the dream. It’s getting to work on an area that brings together the world of computation and the physical world of mechanisms and materials.

KEVIN SCOTT: That is so interesting. I mean, I – it’s hard to imagine what it must have been like as a teenager, but I’ve got to say I’m pretty impressed that you learned to cut threads on a lathe when you were in high school. Like, that’s – that’s one of the coolest things I’ve heard in a while. (Laughter.) So, you – you got all of this experience as a high school student. So, where did you go to college and what did you major in?

DANIELA RUS: So, I went to college at the University of Iowa. My family migrated to the U.S. just around the time I was finishing high school. And as an undergraduate student, I studied computer science, mathematics and astronomy. And then, one day, I had a chance to speak with our then distinguished lecturer, this is Professor John Hopcroft. John is one of the giants and founding fathers of computer science.

So, he gave a talk and after the talk, I had a chance to talk with him, and he told me that classical computer science was solved. Okay, so, by that, he meant that a lot of the algorithmic architecture and systems problems that were posed at the beginning of the field had solutions. And then John said, “It is time for the great applications of computing.” I think he used the word “grand” applications of computing, and the application he was most passionate about at that time was robotics. And he saw robotics as a way of enabling computation to interact with the physical world.

Now that, to me, was absolutely fascinating. It was a tremendous opportunity, and I decided to go and work with John. So, I recall an early conversation with John where he said, “Wouldn’t it be great to have robots that could make and bring coffee?” And this kind of became a challenge for my Ph.D. studies.

But the robots that existed back then were just these big, bulky, large industrial manipulators that could not execute the kind of delicate operation that’s needed to pick up a coffee cup or a teaspoon.

So, John’s coffee challenge enabled me to realize the important role of the body of the machine in the process of generating autonomous behavior. And I started working on algorithms for computing that interacts with the physical world. I did not have much of a chance to – to think about different types of robot bodies, but I was thinking about what a different kind of robot could, in principle, do.

So, I worked on in-hand manipulation, so essentially moving things with your hands. I learned on pushing objects, on rotating objects, on moving furniture. I developed a lot of algorithms that connected computation with the actual movement of the object. And so, even though I could not solve the coffee challenge – (laughter) – I was able to work on various aspects of fine manipulation, planning for dexterous manipulation and multi-robot manipulation.

And it really turned out that the theory was hard and the experiments were even harder. And in fact, the robots couldn’t really implement the theory. We had these results in theory, but we had no robots that could implement them.

And – and so, after that, I became very interested in the connection between body and brain as a way of solving the kind of computation for interacting with the physical world problems that are so prevalent in the field of robotics.

So I want to tell you one more thing about that. So, get this. So, some years later, maybe about two decades later, let’s just say the robots became more dexterous and their computation and the set of algorithms became richer. And then it was my turn to give my students the coffee challenge.

And so, when I said, “Hey, can you make me a robot that would bring me coffee?” they said, “Sure thing.” And it only took a little bit of time to tackle the sort of version of the coffee challenge. They didn’t really solve the whole thing, but they certainly solved way more than I could when I was a student. And so, interestingly, it took over 20 years for the hardware and computation to be able to deliver on the kind of operations required by dexterous manipulation. And we are not even fully there yet.

And I would say that the biggest problems, the biggest technical challenges require this kind of time horizon, require this long ramp to go from science fiction to science and then to reality.

KEVIN SCOTT: Yeah, it’s so interesting. I think what you have been working on over the years is one of the things that still fascinates me, because if I think about how I might go, solve the – or try to solve the coffee challenge, if I were a graduate student now, I would probably strap one of these commodity six axis robotic arms. I would 3D print a custom end effector for it. I’d put it on some sort of mobile cart.

And it feels to me a little bit like that’s sort of cheating, right? That’s still not the dexterous manipulation of objects that humans can do quite easily from the time that they’re infants. So, how do you think about how the field is progressing? So, on the one hand, it’s, like, fantastic that we have all of this robotic technology that is practically solving a bunch of important problems, but we still aren’t quite there yet with full human level dexterity.

DANIELA RUS: Well, absolutely, and this remains a very big challenge in the field. But as you said, we’re making progress and we are also understanding the impact of the various components of the machine for solving the problem. And so, I really believe that in order to solve any meaningful problem, you really need to have the right machine body for that problem. You really need to have the right machine brain and the right interaction with the machine.

So, what do I mean by this? Well, let’s say you want to run GPT3 on your mobile phone. You can’t, because you don’t have a body that’s capable of that task. Let’s say you want a robot that needs to climb stairs, but all you have is a robot on wheels. The robot is not going to be able to deliver on the challenge.

And so, the body plays an important role in how we think about solutions to problems. But once you have a body, you also need to have a brain that’s able to control the body to deliver on what it’s meant to do. And that requires advancements in theory, advancements in algorithms, advancements in systems and architectures.

So, really, we need to connect the two pieces. We need to think about the body and the brain as being connected, as influencing one another. And in my research, I started thinking about design as a process that simultaneously considers the robot body and the robot brain, or the machine body and the machine brain, because these ideas are not limited to robots. Computers also fall under the same category.

So, in some sense, a few years ago, we started working on what we call the robot compiler, where the idea is you give the machine or you give the computational design platform the task you want the machine to do. And what you expect is a solution that involves the hardware configuration, and also the software and programming environment required to deliver the function of that machine.

And oh, and I didn’t tell you about interaction. So, if you want to use the machine, then you have to understand it, right? And that requires that we think seriously about how machines and people interact with one another. And until very recently, in order to use a computer, you really needed to be an expert. Like, if you think about where computing was 20 years ago, 20 years ago, only experts could use computers. You really needed to understand what to do. You needed to have a lot of money because computers were so expensive.

But all that changed with the smartphone, which has democratized computing. And I believe that the same can be true for physical work. And so, we should ask ourselves in this world so changed by computation that helps us with number crunching tasks, what might it be with machines that can help us with cognitive and physical work. How much work can we offload to machines and how would machines interact with us?

Now, I believe that in the future, we will have, increasingly, machines that adapt to people rather than the other way around, because today, we have to adapt to the machines that we use.

KEVIN SCOTT: Yeah, I totally agree. Well, I’m sort of interested, maybe this is a little bit related to what you just said. One of the big changes in artificial intelligence over the course of our careers has been this adoption, particularly over the past, let’s say, 15-20 years, of machine learning techniques.

So, I know when I was in grad school, all of my – I was a compiler and programming languages person, but all of my – my friends who were working on artificial intelligence or robotics were working on planning algorithms, and, you know, a bunch of knowledge-based systems and expert systems, and trying to build these systems that emulated intelligence and that had agency in the physical world by developing algorithms that effectively manipulated symbols and rules, and just had mathematically precise understandings of what they were doing and how they were interacting with the world.

And, like, the thing that’s really changed since I left academia and, like, sort of defined my entire career in industry is, machine learning – where you just sort of have lots and lots of data, and you are trying to train models from those data that allow you to do things. And this has obviously changed how robotics works a little bit.

I’d love, though, to get your perspective on how you’ve seen this change impact your work and – and then there are lots of shortcomings to machine learning technologies as well. And so, I’d love to dig into that a little bit with you.

DANIELA RUS: Well, this is a very big question, Kevin, and I would like to observe a few things. (Laughter.)

First of all, AI, machine learning and robotics are really transforming the scope of problems that we can solve. And they’re pointing to so many opportunities for the future. So, with these technologies, we can imagine a world with no traffic accidents, and no wasted time in congestion. We can imagine a world where everybody has personalized and individualized healthcare, a world where we can better engineer medicines and better monitor, diagnose and treat disease.

We can imagine a world where we can have instantaneous communication between people, no matter what language we speak. We can imagine a world where machines take on the routine tasks, allowing people to focus on the more creative, cognitive and physical tasks.

So, that is the promise, but we are quite far from that promise. So, even though we have tremendous successes due to the advancements in AI, machine learning and robotics, there are also limitations. And I would like to remind us that the successes we see today are due to decades-old ideas that are augmented with tremendous power and data. So this is why they work. They didn’t work 20 years ago, but they work now because we have so much more computing power and so much more data.

And so, I think that it’s important to harness these ideas and deliver the maximum that we can from these ideas, but I also think that it’s super important to think about new ideas. New ideas are really critical to advancements, and without new ideas and also funding to back them, more and more people will be plowing the same field, and that means that the results will be increasingly incremental.

So, we really need major breakthroughs if we’re going to live up to the promise, but also if we’re going to manage the major technical challenges facing the field, the major technical challenges facing how we deploy this technology in a way that is responsible and that ensures the greater good. We also need a computational infrastructure in order to enable progress, and this is an infrastructure that could deliver data and computation to us, like we get utilities, like we get water and energy.

And so, one way to harness this challenge is to look towards the natural world and see if we can learn something by studying the natural world. And this is actually super important, because as we define ourselves as researchers advancing the science and engineering of intelligence or the science and engineering of autonomy, it’s useful to focus both on the science part, meaning understanding how the natural world works and also on using the new insights, let’s say, to create new types of machines to create new solutions.

I think there is a lot to be learned, but there is also lots to be discovering from the world. We know so little about intelligence and understanding natural intelligence remains one of the most profound problems that is facing humanity today. There are so many other problems, including understanding what we can do to save the planet, understanding how to be equitable about the use of technology and understanding how to ensure sustainability. But understanding life is among these very important problems.

And so, I think that people who study the science and engineering of autonomy or the science and engineering of intelligence can get new insights about life, about ourselves, and then we can harness those insights to create different types of machines. So, this doesn’t mean that what we have now is not sufficient. What we have now is fantastic. It’s great, we can harness, but we also can do more and this requires innovation.

KEVIN SCOTT: Yeah. Yeah, I very, very, very strongly agree with that. There are a lot of interesting things I think that we can still discover by trying to exploit scale in some of these problems, but we do have this widening gap, I think, between how much computation is involved in training a big machine learning model versus, you know, like just how much energy you expend to train one of these models versus the quiescent energy the biological brain consumes to, like, implement its intelligence.

And so, like, that’s a really inspiring gap there. I mean, there’s much, much to be discovered between these systems that we’re building that aren’t yet fully intelligent. And these biological systems that are.

DANIELA RUS: Yeah, I completely agree, Kevin. I mean, just think about what a kid can do on a chocolate bar, right? (Laughter.) So, like, you give a kid a chocolate bar and then you have hours and hours of extraordinary intelligent and autonomous activity. And that’s, compared to that, if we look at what our machines need, if we look at the cost of training a model like the GPT3, it’s really extraordinary.

And honestly, as we move forward, I really believe we need to get serious about sustainable AI, and I believe we can actually have good solutions.

So, you pointed out that the machine learning models are getting bigger and bigger, and the data needed to train them is greater and greater. And this is because the more data the system sees, the better the scope or the bigger the scope of the system, but that uses a lot of energy. That process results in huge amounts of energy and huge amounts of carbon dioxide that is a result of doing those computations.

For instance, the researchers at the University of Massachusetts at Amherst estimated that training a largish deep learning model produces 626,000 pounds of carbon dioxide. This is equivalent to the lifetime emissions of five cars, and that’s just one average model.

Now, it costs $4.6 million in energy costs to train GPT3. And so, if I think about it, I have fifty to a hundred models that are being trained in my lab right now, and that’s just one researcher. If we think about all the activity that is going on in the space, it’s challenging, right? I mean, so we have to be responsible and responsive to the needs of the planet.

And so, this is an area where I think technological innovation can truly contribute and extend the scope of our tools. So, the systems are so costly because each system contains hundreds of thousands of neurons and billions of interconnections, but if you can develop simpler models, this can drastically reduce the carbon footprint of AI.

And this isn’t really a hypothetical statement. It’s an area where many MIT researchers are already making progress. They are making progress in multiple ways. We are looking at how we can take a huge machine learning model and compress it so that we throw out all the redundancies in the model. And we have shown that we can throw out up to 90 percent of the parameters and still get approximately the same performance. So, that’s already good. We are making good progress towards protecting our planet.

We also are looking at inspiration from the natural world to create more compact solutions from the beginning. And so, in particular, my group has been developing a mathematical structure we call neural circuit policies, and the structure is inspired by nature. It’s inspired by the neural systems of small creatures like worms that have been studied by biologists to the point where the entire neural structure is understood and characterized.

And so, for instance, C. elegans, a very small organism, has 300 or so neurons, and on 300 neurons, this worm lives a good life. Right? The worm finds food, moves in the world, reproduces, and that’s really extraordinary. That’s much less than what we have in our machine learning models.

But each neuron you employed by this creature is actually a pretty complex mathematical function. It turns out that the function of the neurons was characterized to be differential equations, not the typical step function that is used in deep neural networks. And so, the question is, can we, from the get go –

KEVIN SCOTT: That’s interesting.

DANIELA RUS: Create models where the neurons are more capable, where the neurons can compute more than a step function?

And so, in our research with neural circuit policies, we have done exactly that. We have allowed our neurons to compute what we call liquid time differential equations. So, these are differential equations where time can be varying, so time is not constant. And we have also allowed that we have specialized neurons. And we observed really interesting effects.

And so, for instance, we have looked at what it takes to learn from a human how to drive a car, how to steer a car, and it means how to accelerate and how to steer. And we built a deep neural network model that required about a hundred neurons and a half a million parameters, and we had pretty good performance.

But then we also built a neural circuit policy solution, and our solution has 19 neurons. And with these 19 neurons, we are so much more able to visualize what is happening in the engine, in the black box. We can create a decision tree that’s associated with that computation. And then we have kind of an understanding of how the engine does what it does, and this is important for safety-critical applications.

KEVIN SCOTT: Yeah, that’s really, really fascinating. I’d love to learn more about that work.

So, just switching gears for just a second, you run the MIT Computer Science and AI lab, which is one of the iconic computer science research institutions. So, tell us a little bit about what it’s like to do your job.

DANIELA RUS: Well, Kevin, I am honored and humbled to be able to work with such brilliant colleagues and students at MIT and at CSAIL. Everyone here is advancing computer science with the objective to contribute towards the future of computing and to making the world better through computing. And through this work, there are foundational contributions to computing, but also there is a lot of inspiration for applications and businesses.

And so, being part of this community is inspiring and mind bending. The community has an extraordinary history. And you can think of CSAIL as having two parts, CS for computer science and AI for artificial intelligence.

The AI part of our name goes back to 1956 when Marvin Minsky decided one summer to gather his friends. They went to the woods of New Hampshire. They spent a month discussing the deepest questions in science, and when they emerged from the woods, they told the world, “We coined a new field of study, artificial intelligence,” which is about the science and engineering of creating machines with human-like characteristics in how they move in the world, how they see the world, how they play games, even how they learn.

And so, our members have been advancing these ideas from the very beginning with such imagination and insight.

The CS side has an equally proud history that goes back to 1963 when the big dream was for two people to use the same computer at the same time, right? (Laughter.) And those computers were about as big as the rooms we sit in. And so, can you imagine in 60 short years, we moved from dreaming that two people might use the same gigantic machine to a world where everybody computes, everybody benefits, through smartphones and other means of computation. And so, it’s really extraordinary.

So, I will tell you that CSAIL has always been about moonshots, about big dreams, about how to go from science fiction to science and then reality, and this is so inspiring. And for our students and researchers, no question is too crazy. No future is too far off. Everyone takes pride in imagining the impossible and then finding ways to make it possible.

And so, it’s really an extraordinary privilege to be part of this tradition and to have a chance to develop programs and opportunities to support the dreams of our researchers.

KEVIN SCOTT: Yeah, so I’d love to hear what you think some of those big moonshots are. I know one of the things that I’m fascinated by that you’re working on is soft robotics. So, if you could tell us a little bit about that and, like, any other big, interesting things that you all are working on, we’d love to hear about.

DANIELA RUS: Well, let me say that there are things I work on as a researcher, and then there are things that – that CSAIL, as a community, works on together. So, CSAIL is a very large community. There are 125 faculty here and 1,300 members. And so there’s just a lot of a lot of activity.

Now, one of my passions is to bring machines, materials and people closer together. I want to have more intelligent materials, and at the same time, I want to have more flexible, safer, more dexterous machines. And one way to think about this is to consider what robots were like when they were introduced in 1961, or 60 years ago. The first industrial robot was Unimate. It was introduced in 1961 and it was invented to do industrial pick and place operations.

Now, since then, the number of industrial robots in production reached tens of millions. And these industrial robots are true masterpieces of engineering that can do so much more than people do. And yet, these robots remain isolated from people on the factory floor because they’re large, and heavy and dangerous to be around. So, we’d like to have machines that are safer to be around, and that can be teammates for people.

Now, if we compare industrial robots with organisms in nature, organisms in nature are soft, and safe, and compliant, and more dexterous and more intelligent. How can we get to the point where we have robots that are like that?

And so, as I think about our interaction with machines and the natural world, I actually feel inspired to rethink what a robot is, because while the past 60 years have defined the field of industrial robots and empowered hard bodied robots to execute complex assembly tasks in industrial settings, I really wish for the next 60 years to be ushering in robots for human-centric environments and robots that can help people with cognitive and physical tasks.

Now, as we think about what these robots might look like, I’d like to ask us to look back at what our current robots looked like. So, when you think about a robot today, the images that come to mind are like an industrial manipulator, a humanoid or a box on wheels, right? These are the robots that are most used today. And so, these robots are primarily inspired by the human form or by boxes on wheels. (Laughter.)

And so, what I believe is that I believe that we can do more than that. I believe that we can stretch ourselves and go to a different stage, where we think about soft robots that are inspired in shape by the animal kingdom with its form diversity, by the natural world, with its form diversity, and even by the built environment, because then, we would have so much more potential for applications.

I also believe that we can consider a wider range of materials that we have available to us to make these extraordinary machines. The robots of the past 60 years have been made mostly by hard plastics and metal, but what about machines that are made out of all materials available to us? And so, we can consider plastic, and silicone, and wood and paper, even food, and we can also consider synthesized materials. I think there is so much opportunity to create a whole new type of machine that will be a good teammate for people, that will be a more capable tool for people who need help with physical and cognitive work.

KEVIN SCOTT: Yeah, I’m really excited about the possibility. So, it feels like we’re at this point in time where we – we’re really ripe for new breakthroughs.

You know I’m a hobbyist machinist and one of the things that I’m seeing in a bunch of machine shops now, and one of the things that people are thinking more and more about, is how to integrate simple things, like six axis robotic arms, into their workflows. So, how you can have a thing that will pick a raw piece of metal up, you know, open a door on a milling machine, like, place it into a fixture in the machine, like cycle start. You know, the part gets made and then you sort of reverse the whole process. You pull the finished part out, put it on a pallet.

And, like, that can be an amazing thing in some of these shops, where you can sort of run an extra shift and keep these really expensive machines running all the time, but they are sort of simple things. You know, you program them by basically having a human guide them through a bunch of waypoints in the process you want them to accomplish. And you usually are custom designing some sort of end effector so that it can pick up the things you want it to pick up.

But it’s really exciting to think about things that aren’t that simple that have, like, really complicated, dexterous end effectors and that can be programmed in more robust ways.

DANIELA RUS: Well, and Kevin, let’s even go beyond that. Let’s even bring more cognition to these tools. And let’s say that these tools, thesemachines will be able to watch you, and understand what you want to do, and come and give you a hand. So, let’s say you’re trying to lift a heavy box and a machine comes to help you lift it up just like a friend would today.

KEVIN SCOTT: Yeah. That is – that’s a great vision, and I’m so glad you all are working on these things.

And I’m just curious, what sorts of technological breakthroughs do you think we are going to have to have in order for some of these things to happen? You know we’ve got a bunch of things that have gotten really cheap over the past handful of years, and those – or things where we clearly understand what the scaling path looks like.

But what breakthroughs do you think we are waiting for in order to see this next round of progress, like to have a machine that could do what you just said, that can notice that I’m struggling to pick something up, and come to assist me?

DANIELA RUS: Well, Kevin, we will need progress in all aspects of using the machine. And so, remember that the machine has a body, the machine has a brain and the machine has the interaction with people. So, we need progress on how we build and design machines. And here, I think computation, machine learning, AI can play a tremendous role. I think we’re slowly moving towards computational design and computational fabrication of machines.

And this allows us to experiment with different types of design. It allows us to experiment in simulation so that the time required to create the final product is greatly reduced. This is back to our robot compiler that I mentioned earlier in our conversation. And as we think about designing these machines we have to consider the shape, we have to consider the materials, we have to consider energy requirements. So, we also need to think about the energy aspect of the machine.

In fact, every time I’m stuck in traffic, I actually dream about my car lifting up in the third dimension and buzzing above everybody, so that I don’t have to be late for my meeting. (Laughter.) And right now, that vision can be, will be achievable if we can figure out the energy solution, because honestly, thinking about a machine flying through free space is simpler from a computational point of view than a self-driving car that has to drive through congested traffic. So, I’m excited about those possibilities.

So those are technical challenges on the hardware side. We also need to consider challenges on the computational side or what I call the brain. So we need to make more capable algorithms, more capable machine learning solutions, more compact machine learning solutions, more solutions that can deliver safety-critical algorithms that are characterizable with respect to what they’re able to do.

And then we need advancements on how machines and people interact with one another, so that we can have more intuitive interactions that will allow people at large to use machines without needing to become experts in robotics.

KEVIN SCOTT: So, as you look forward to the next five, 10, 20 years, what are you most excited about?

DANIELA RUS: Well, I’m really most excited about this vision of building machines that truly help people with cognitive and physical work, and doing that in the process of advancing the science and engineering of intelligence, or if that’s too ambitious, maybe it’s at least the science and engineering of autonomy.

And so I think that there are so many really exciting problems with respect to automating how we think about the hardware piece of the machine, expanding the capabilities of the machine, thinking deeply about how machines interact with each other and interact with people. This is all tremendously important.

But I think that as we think about these challenges, it is also important to consider how the challenges benefit people and humanity, in general. And so we talked about positive impact in healthcare, positive impact in transportation, positive impact in so many aspects of our lives. I think that AI and computation holds so much potential to help, but we actually have to do it very carefully.

And so, for instance, in healthcare, there is tremendous potential to improve diagnosis. In fact, machines today will look at more data in a day than a doctor will see in a lifetime. And there was a fairly recent experiment where an AI-based approach and a doctor were tasked to look at scans of lymph node cells and diagnose cancer or not cancer.

And it’s interesting to see that the machine had an error rate of 7.5 percent. The doctor had an error rate of 3.5 percent. But when the machine – when the AI system and the doctor worked together, the error rate went down by 80 percent to 0.5 percent. This is really substantive.

And so, today, these systems may be deployed in the world’s most advanced cancer treatment centers, but imagine a future where every practitioner, even those working in small practices in rural settings, had access to these systems. An overworked doctor may not have time to review every new clinical study, every new paper, but these systems could provide the doctor with pointers that will enable the doctor to offer patients the most cutting-edge diagnosis and treatment options.

But now I’m rushing because I want to make sure we realize that this work requires data. And every time you need data, you need to consider the risks to privacy. And here, regulation can help, but there are also potential advancements in a technological context. So, technological breakthroughs will help us get through the issues of privacy in a much faster, much easier way, and in fact, technological breakthroughs can help with broad challenges in computation, like the cybersecurity challenges that we see every day when we turn on the news.

And here, what I mean is advancements in homomorphic encryption that can enable us to compute on encrypted data without decrypting it, so that organizations that need information, like your insurance company, for example, can post queries, can use the data without actually looking at the details.

And so I’m so excited about the possibility for technology to work closely with policy and with leaders of businesses to get to the point where we have safe and responsible deployment of technology.

KEVIN SCOTT: Yeah, I really agree with everything that you just said, and there’s a bunch to be inspired by. We’ve been excited about homomorphic encryption and the general bag of techniques to make sure that you are getting all of the benefits from machine learning and AI, without having to make the tradeoffs that you mentioned on making the data available to the model versus privacy quite as stark as they are right now, or they can be in some cases.

But, like, maybe the most inspiring thing that you just said is this example of a machine learning system and a doctor working together to get to a fundamentally better outcome than either of them could achieve on their own. I think this cooperative AI or collaborative AI is really an exciting thing to think about.

DANIELA RUS: I like to think of our machine learning solutions of today as sort of like interns. They’re running in the background. They’re – they’re doing tasks, but then they’re bringing the results to the human to make an informed decision on. And I think that that’s the best way to use AI and machine learning in – especially in safety-critical applications.

KEVIN SCOTT: Yeah, very, very, very strongly agree with that.

So, we are running out of time here. But before we end, I would love to ask you a question I ask everyone, which is what do you do in your, quote/unquote, spare time? (Laughter.) I’m sure you’re one of the busier people on the planet, and you’ve got lots of fun stuff to occupy your time when you are working. But what do you do when you aren’t?

DANIELA RUS: Well, I actually like to spend time with family and friends. (Laughter.) I also like to enjoy nature, and I love to ski in the winter. I love to scuba dive in the summer. I’m trying to get back into tennis. I used to play tennis. I used to be pretty decent, but I haven’t played in about 20 years. I love to cook.


DANIELA RUS: I love to host dinner parties, where we could have conversations like we had today, Kevin. But this has been kind of tough during the pandemic.

KEVIN SCOTT: Yeah, it has been. What’s your favorite thing to cook?

DANIELA RUS: Oh, that’s like asking, who’s your favorite child? (Laughter.) So, I actually have a wide range of things I like to make. So, for my girls, I like to make my very special meat sauce pasta that they enjoy every time. But I also like to get a little bit fancy and make higher skill recipes that mostly come from French cookbooks, or I really like the cookbooks of Annabel Langbein. She’s a New Zealander chef who has really, really wonderful cookbooks.

In fact, sometimes after a long day at work, I might pick one of her books before I go to sleep. And I might just read one of her recipes as a way of relaxing. And then what that does is it creates a kind of a cache of ideas. So, oftentimes, when I’m in the kitchen, I actually don’t follow a cookbook. I just look at the ingredients in the fridge and I make whatever I dream up on the spot.

KEVIN SCOTT: I love it. That sounds like so much fun. So, thank you so much for taking time to chat with us today. I think you’ve given us a ton to think about, and I am so glad that you are at MIT in CSAIL, doing the work that you’re doing. It was really great to have you on the show.

DANIELA RUS: Thank you so much, Kevin.


CHRISTINA WARREN: Well, that was Kevin’s conversation with Daniela Rus. Okay, so I have to start with this. I kind of, I’m still trying to wrap my mind around the fact that as a kid, she grew up watching Lost In Space, which I have to admit I’ve never seen. But I know the memes, “Danger, Will Robinson.” I know that it has something to do with robots. There are robots in it. She grew up watching this show in Romania, and now, she literally builds robots and works on kind of the future of this stuff. How cool is that?

KEVIN SCOTT: That is super cool, and it goes to this point that I make a lot that inspiration and role models are so important when you’re a little kid.


KEVIN SCOTT: I think a lot of people of her generation and mine are doing what we do today because of the TV shows, and the science fiction books, and the things that the news media was consumed with talking about, when we were little kids thinking about what we wanted to be when we were growing up.

CHRISTINA WARREN: Yeah, no, I think we’ve talked about this before on other episodes, but kind of showing off like what that future world might look like, encourages and kind of creates opportunities for technologists to actually go out and make those things a reality, which I mean, I just – I love that because now, you know, she’s literally working on the future of how, you know, robotics is such a fascinating space.

And there are so many interesting implications, as you both were chatting about. And I – I love that that is coming out of, you know, as you were just kind of saying, like, having that role model, having that modeling, I guess you could say, for someone when they’re – when they’re younger.

KEVIN SCOTT: Yeah. The other thing, too, that was fascinating to me is that she was working in a factory for, you know, I think, a week out of a month, and learning how to use a manual lathe, and single point turning threads. Like, all of this stuff is so awesome, and I think – she mentioned it herself – Like, one of the reasons why she thinks about the work that she does as a mash up across a bunch of different disciplines. And I probably would have been absolutely unenthused to be forced to go work in a factory when I was a teenager, but, like, it – it had to have been a really interesting, formative part of her experience as a kid growing up.

CHRISTINA WARREN: Yeah, I was struck by that, too, because as she said, you know, she said that it kind of really did inform and contribute to her journey now. But yeah, like you, I would have been completely unenthused. But when you look at the work she’s done, having that experience and having, like, that fundamental understanding of how those parts work, and having that experience of seeing that machinery up close, actually touching and feeling it, you know, as a teenager, had to have an impact then when you’re creating these – these other types of machines, right?

KEVIN SCOTT: Yeah. You can sort of see a thread that runs through a bunch of our guests, and I suspect it runs through you and I as well. You know, a lot of people who end up in tech are super curious about a pretty broad range of things. And they also, pretty early on, learn that the world doesn’t work on magic. (Laughter.)

CHRISTINA WARREN: (Laughter.) Right.

KEVIN SCOTT: You know, the world works because lots and lots and lots of scientists, and engineers, and scholars and people of all walks of life have come together to build really complicated things. And if you know where to look, you can figure out how anything operates. And, like, that’s an important piece of, I think, the mindset that she probably had inculcated in her, like, pretty young, because she was working in a factory.

CHRISTINA WARREN: Yeah. Yeah, no, I think so. And I think it’s also an interesting perspective to bring, you know, having that – kind of that – that wonderment and that curiosity when you’re teaching students, when you’re overseeing, you know, this group of people at MIT.

I wanted to ask you, you know, towards the end of the conversation, you talked a little bit about maybe kind of the future of what – what might happen next. And she was talking about some of the different materials and things that might be used in robotics, and whatnot. But I’m just curious, from your perspective, what is exciting to you in the robotics space, specifically, coming around the future?

KEVIN SCOTT: I’m excited at how fast the technology is evolving and has been evolving over the past couple of decades. So, one of the things that is pretty thrilling to me is to see robotics and automation being used in rural communities to revitalize the economies in these places to sort of give some of these communities, likea new lease on life because you – through the leverage of all of that automation – you can take the workforce that is available in these places and do an awful lot with it, which is, to me, really incredible.

And it’s a thing that I think we have to get better and better at in the future, because you sort of look at the demographics of most of the industrialized world right now, and we have a growing population of retired people and a smaller population of working age people coming in behind them. And you need lots of this robotics and automation and technology, in general, so that the world can keep on functioning. And so, I’m excited to see that I think the technology is actually up to the challenge ahead of us.

CHRISTINA WARREN: Yeah, no, I totally agree with you. I think that is really interesting. And like you said, it can bring kind of a new lease on life, so to speak, to some of these communities. And I think that’s an interesting kind of turn on the head of kind of the typical, what has been one of the narratives of the last more than 50 years of, you know, the robots are going to take over the world and seeing it as a negative, and instead, no, actually, this automation could, as you said, help when we have a workforce that is in industrial parts of the world that are you know, more people are retired, less people are working, but we still need to get things done.

KEVIN SCOTT: Yeah. I know as a young engineer earlier in my career, every time I was able to build a piece of automation to help me with a job that I was doing, it was because the job that I was doing was repetitive and annoying. (Laughter.)

CHRISTINA WARREN: Yeah. I mean, look, I’m – I will be the first to admit I’ve been very, very guilty of spending three times as long to automate a solution than to actually go through the repetitive task over and over again, just on the off chance that I might need to use that script or automation again in the future.

KEVIN SCOTT: It is a virtue of a good programmer. (Laughter.)

CHRISTINA WARREN: (Laughter.) All right. Well, that was a fantastic conversation with Daniela. We are out of time for today. Thank you again, Daniela, for sharing her time, and her amazing insights and her great story with us.

Remember, we always love to hear your ideas for guests, so please e-mail us any time at [email protected]. Thanks for listening.

KEVIN SCOTT: See you next time.