{"id":11110,"date":"2026-04-21T21:39:05","date_gmt":"2026-04-21T21:39:05","guid":{"rendered":"https:\/\/www.europesays.com\/ai\/11110\/"},"modified":"2026-04-21T21:39:05","modified_gmt":"2026-04-21T21:39:05","slug":"google-deepminds-learnings-in-developing-an-ai-tutor-the-74","status":"publish","type":"post","link":"https:\/\/www.europesays.com\/ai\/11110\/","title":{"rendered":"Google DeepMind\u2019s Learnings in Developing an AI Tutor \u2013 The 74"},"content":{"rendered":"

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Class Disrupted is an education podcast featuring author Michael Horn and Futre\u2019s Diane Tavenner in conversation with educators, school leaders, students and other members of school communities as they investigate the challenges facing the education system in the aftermath of the pandemic \u2014 and where we should go from here. Find every episode by bookmarking our Class Disrupted page<\/a> or subscribing on Apple Podcasts<\/a>, Google Play<\/a> or Spotify<\/a>.<\/p>\n

In this episode of Class Disrupted, Irina Jurenka, the research lead for AI in education at Google DeepMind, joins Michael Horn and Diane Tavenner to discuss the development and impact of AI tutors in learning. The conversation dives into how generative AI, specifically the Gemini model, is adapting to support pedagogical principles and foster more effective learning experiences. Jurenka shares insights from her team\u2019s foundational research, the evolution of AI models over the past three years, and the challenges of aligning AI tutoring with learning sciences. She reflects on how these innovations may shape the next generation \u2014 with hope for a thoughtful blending of technology with the irreplaceable role of human teachers.<\/p>\n

Listen to the episode below. A full transcript follows.<\/p>\n

Diane Tavenner: Hey, this is Diane, and you\u2019re about to hear a conversation that Michael and I had with Irina Jurenka from Google DeepMind. She\u2019s the AI research lead for education there, and I think you\u2019re going to love this conversation. It was fascinating for us to talk with someone who is literally working on the large language models from the education perspective, and at Google, no less, one of the most ubiquitous ed tech products in the world at this point, and her perspective on where AI is going, where her work is going, how it\u2019s going to be, how she imagines it\u2019s going to transform schools or not transform schools, and what\u2019s important. Turns out to be a really interesting dialogue. I think you\u2019re going to love it.<\/p>\n

Diane Tavenner: Hey, Michael.<\/p>\n

Michael Horn: Hey, Diane. It\u2019s good to see you.<\/p>\n

Diane Tavenner: It\u2019s good to see you, Michael. I\u2019m really excited for the conversation we\u2019re going to have today. I find that while almost everyone is talking about AI, almost no one seems to know what they\u2019re actually talking about, especially in the circles that I think we sometimes run in. And so I\u2019ve always found that technology is a bit of a black box to many educators, and I think AI is exacerbating that. But today we get to talk with someone who works on and in the black box, if you will, and understands its intersection with learning. She just understands that just about as well as anyone I know. And so bringing both of them together is Irina Jurenka, and she\u2019s joining us on the show today. Welcome, Irina.<\/p>\n

Irina Jurenka: Thank you.<\/p>\n

Diane Tavenner: Irina is the research lead for AI in education at Google DeepMind, and we\u2019ll unpack all that in a minute to help people understand what that means there. She\u2019s exploring how generative AI can truly enhance teaching and learning. And it\u2019s not just by providing answers, but also by helping people learn more effectively and equitably. She recently led a landmark study called Towards Responsible Development of Generative AI for Education, which looks at what it takes to design AI tutors that are actually good teachers. Before DeepMind, Irina earned her doctorate in computational neuroscience at Oxford, studying how the brain processes speech and learning. Her work beautifully bridges neuroscience, machine learning and education, all in the service of a simple but powerful goal, helping every learner reach their potential. We\u2019re so excited to be in dialogue here with you, Irina, welcome.<\/p>\n

Irina Jurenka: Thank you. I\u2019m really excited to be here.<\/p>\n

AI for Equitable Education<\/p>\n

Diane Tavenner: I thought we would just start with some really basic things to help people understand what you do. And so let me start with asking, is it fair to say that you\u2019re both a learning scientist and a technologist? Is that how you think of yourself and will you explain to us what a research engineer does or is and help us to understand sort of your team and what you do?<\/p>\n

Irina Jurenka: Of course. So I actually don\u2019t think of myself as a learning scientist. I would say maybe I\u2019m a beginner learning scientist. I\u2019m definitely just starting to learn about this field, but I\u2019m very lucky to be working in a company where we do have learning scientists on the team, and we also work very closely with teachers. So we actually just hired a teacher on the team and there is another teacher who is consulting us, and we work closely with the academic field as well. So Kim Collinger and others are advising us, which we\u2019re very privileged to be in a position to have such amazing advisors. My role in education is relatively recent. I only started this project around three years ago.<\/p>\n

Diane Tavenner: You know, we hear the term research engineer and you\u2019re a research lead. What does that mean? You know, I think a lot of us are accustomed to the terms, you know, software engineer, but in the age of AI now we hear this term research engineer. So I\u2019m wondering if you can help us understand.<\/p>\n

Irina Jurenka: Of course. So I work at Google DeepMind, right. And DeepMind has always been effectively an academic lab. So when I joined 10 years ago, it was a very small group, it was incredibly academic. So I joined as a research scientist and essentially my job was to do\u00a0 foundational AI research and publish papers. That\u2019s kind of where we\u2019re coming from. And now we are kind of much more integrated within Google, but we continue on the same mission. So what DeepMind brings to Google is this research expertise.<\/p>\n

So on my team we have scientists and engineers, but really like the line between them is blurred. And what our job is to really think about what are the fundamental scientific problems around language models and in our case, on intersection with education, where we really need to do this foundational scientific work to understand what are the big problems, how do we find tractable solutions and also work out the solutions to these kind of big scientific problems.<\/p>\n

Diane Tavenner: So I guess one question I know, Michael, that has been coming up for you and some of the conversations you\u2019ve been having is do you engage with or interact with or directly influence the products at Google? So many of us in education are so familiar with so many Google products and what is the intersection of your work and for example, Google Classroom or many of the other products that we in the education field use?<\/p>\n

Irina Jurenka: So I find it very exciting to be in a company as big as Google where there are so many amazing teams doing incredible work. We really focus on the research because that\u2019s the value that, you know, my team can bring. We do work closely obviously with the products because they build on top of the foundational models. In our case, Gemini. We talk, we advise, we help explain kind of what Gemini is capable of, how to best elicit the kind of more pedagogical capabilities out of it. But of course the teams are amazing and they mostly bring kind of work on the products in their own teams.<\/p>\n

Diane Tavenner: Got it. That makes sense. It\u2019s been just about three years since most of us in the world were first really introduced to AI via the first release of, or not the first, the one that we\u2019re familiar with of ChatGPT, and you shared with me in previous conversations that the arc of your team\u2019s work over those three years has been really interesting. You just referenced the three years again. Tell us what you\u2019ve been working on and what\u2019s emerging from that.<\/p>\n

Irina Jurenka: Yeah, we actually started the project about six months before the ChatGPT moment. So, yeah, things have definitely changed for us. Some things stayed the same. So from the beginning, we saw that the biggest impact we could make, or Gen AI, the newly emerging Gen AI we thought could make, was through AI tutoring. There are, I can go into details, like why we think it\u2019s the most impactful thing, but maybe we can address that later. But when we started, language models were very different. Yeah, so I remember doing our first demo about six months after we started, and it was very hard to keep the AI on track.<\/p>\n

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RelatedAI Tutors, With a Little Human Help, Offer \u2018Reliable\u2019 Instruction, Study Finds<\/p>\n

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Advancements in Guided Learning<\/p>\n

Irina Jurenka: So we had to practice a lot, find the right kind of queries to ask. And even then, you know, it was always. We had to be at the tip of our seat just to make sure that it doesn\u2019t go off the rails when doing a demo. And of course, now the AI is so much more powerful. You know, we have launched products like Guided Learning on Gemini app, which millions of users are already engaging with, and it\u2019s mostly staying on track. You know, we haven\u2019t seen any major problems so far, so it\u2019s kind of just the technology itself has changed so much and we kind of had to keep up with these things. So when we first started, a lot of our work was trying to deal with very rough language models and make them do something useful in learning. And you will know how the stakes are so much higher in learning than in other use cases.<\/p>\n

So we really had to think, how do we control this kind of unruly beast beneath us? And now, of course, you know, a lot of that work was essentially had to bin because it was no longer necessary. And we really concentrate on how do we bring the layer of pedagogy and adherence to learning science principles into Gemini to make sure that it really works towards increasing learning outcomes rather than negating them.<\/p>\n

Michael Horn: Irina, I\u2019d love to jump in there because at first I think it\u2019s fascinating that you guys do this much foundational research, because we always hear that sort of the domain of the universities. But here\u2019s DeepMind and then Google after the acquisition, right? Investing in over a decade of foundational research. There\u2019s nothing near term about that. And I\u2019m interested in this work on the tutoring because a lot of the sort of critics, I guess I would say of the AI tutors are sort of one of two approaches. Either oh, who\u2019s actually going to use that? Why won\u2019t they just default to Gemini straight on? Right. And two, even when they do use it, it\u2019s maybe overly procedural, is the critique I hear a lot.<\/p>\n

And so I\u2019m sort of curious, what are you learning about the actual usage in the wild? What are the guardrails that you\u2019ve thought are important? What\u2019s been surprising against those critiques that are everywhere these days?<\/p>\n

Irina Jurenka: Yes, thank you for asking this. We are definitely very aware of the negative perceptions and maybe negative use cases of chatbots in learning. So I was just reading an article from the Guardian earlier today where they surveyed 8 to 18 year olds in the UK and what was interesting, I think just over 60% of first responders said that they perceived AI as being a negative addition to their learning journeys. And it\u2019s for all the reasons that we\u2019re already aware of. AI is just too keen to give away answers. It takes away the cognitive load. Well, not like it takes away the productive struggle. It leads to kind of cognitive offloading of tasks.<\/p>\n

We know that that\u2019s not helpful for learning. So we kind of saw this trend when we started this project because I think at the end of the day, AI is optimized to be an assistant, right? So it\u2019s successful when it takes away the burden from you as the user. And we know that in learning the opposite is true. You have to engage, you have to put in the struggle to actually see a difference to your learning outcomes. So what we realize is that if we don\u2019t do the foundational work and the research to make sure that AI can deal with these two very different use cases, the kind of capabilities of AI in learning won\u2019t just emerge from all the other work.<\/p>\n

Michael Horn: It\u2019s so interesting. So stay with it then, like as you\u2019ve been putting it in this very specific use case right around the tutor. I am curious, like why did you choose tutoring given that it is so different from the other LLMs. Right. Sort of that assistant purpose. And how are you constraining it to make sure that it\u2019s the most useful tutor. Right. That a student could have access to as opposed to maybe its natural instincts based on its foundation?<\/p>\n

Irina Jurenka: So we chose the tutoring use case because the way we call it, it\u2019s kind of learning or education complete. So what that means is that in order to tutor well, you kind of need to know all the different types of subtasks or capabilities that are important for education. So you need to be able to plan a lesson, you need to be able to ask good questions and provide good feedback and check the students\u2019 work, among many other kinds of things about metacognition and active engagement. So if we really manage to figure out the tutoring use case, then the resulting kind of underlying model Gemini can then be used for all these other tasks. A great way to bring one single goal to optimize for that can then result in broader benefits for learning.<\/p>\n

Michael Horn: Super interesting. Let me ask this question then. How does it integrate with Google Classroom? Because you all have this incredible install base effectively right across schools. I think probably in terms of K-12 schools, you\u2019ll correct me if I\u2019m wrong, but I think the largest install base of sort of learning management system instances. So how does this tutor that you\u2019ve built, how is it integrating with Google Classroom to actually directly serve students? And what are the guardrails you\u2019re putting around that as well?<\/p>\n

Safety and Educational Focus<\/p>\n

Irina Jurenka: So in terms of guardrails, I just want to say that we really take this very seriously. There\u2019s a wide range of safety and guardrails work happening across Google DeepMind and Google at all levels from the model to the products. And Gemini in itself has a lot of safety and kind of trust and safety work going there. What our team actually does is bring an educational and learning specific angle into this Gemini model. As an example, when we try to optimize the model for tutoring, we kind of realize that a good tutor really engages the learner. They ask a lot of questions. So we brought this bias towards asking more questions to the model, but that resulted in an unintended consequence in the sense that not only does the tutor want to ask questions, it also wants to encourage questions from the student. And then a student might ask a question that\u2019s actually harmful, so it could say something really toxic.<\/p>\n

Ask a question about that and what the tutor would do before we did the kind of work to mitigate it. It would say, oh, that\u2019s an amazing question. I\u2019m really glad you\u2019re thinking about this. And now and then it will kind of bring it back and say, actually maybe there are other things to consider here. But that initial statement was just not helpful. So we had to then go in and kind of bring extra supervised data and kind of take that unintended behavior out of the model to make sure that it\u2019s actually safe. This additional layer of work is really important. And of course then there\u2019s the product layers and other ways to kind of mitigate safety issues.<\/p>\n

Diane Tavenner: That\u2019s super interesting. I so appreciate the example. And I was gonna ask you about, you know, what is this? What are you seeing now, three years in? You know, you talked about at the beginning you had these sort of rough models and they would kind of, it sounded to me like get distracted and kind of, you know, go off. But, three years in, it sounds like you\u2019re learning a lot of things and so you\u2019re iterating. So what is it looking like now? And how are you feeling about the learning that\u2019s happening when young people are engaging or people are engaging with the products now? And then maybe we can talk about where you think it\u2019s going as well.<\/p>\n

Gemini: Guided Learning Experience<\/p>\n

Irina Jurenka: The work that we\u2019ve done is on the Gemini model side. What we hope that comes out of it is that Gemini is useful for learning products, both for Google, but also for external parties who build ad tags, who build on top of Google. For the internal products, our team in particular really worked in collaboration with the Gemini app to bring the guided learning experience to users. We really wanted to bring an easy way for anyone out there to get kind of this more pedagogical behavior out of the models without having to engineer a very complex prompt. And so with guided learning, it\u2019s really a one click way to get the model to act more like a tutor, so to guide you through the information rather than just give it to you as a wall of text. And we worked with learning science experts to make sure that this experience really adheres to the five kinds of learning science principles that we have identified as important. Again, our hope is that this actually helps students internalize the information much better. And we are working kind of very closely to try to measure the efficacy of how well that\u2019s actually coming together.<\/p>\n

But what I want to give you is a personal anecdote on how I ended up trying guided learning. And I was actually in Stanford a few months ago and I saw this statue of the Burghers of Calais, which I\u2019ve never heard of that story before, so I was curious to learn more. So first I kind of just pulled out my phone and used Gemini to ask about this historical event. And it just gave me kind of the standard answer of kind of a longish response. Think of it as like a Wikipedia kind of type answer. So I read through it, it was interesting, but I realized I\u2019m actually personally really bad with history, in fact. So I realized that that information went kind of into my head and immediately left it, and I didn\u2019t remember anything. About 10 minutes later, I was trying to tell the story to somebody else, and I realized I don\u2019t remember anything.<\/p>\n

So I again pulled out Gemini, but this time I switched on guided learning to see how different the experience would be. The difference is like guided learning doesn\u2019t just give you the answer. It kind of engages you in a dialogue. It brings you in over kind of maybe five to ten turns of conversation. It kind of walked me through the same information, but this time I realized actually that I remembered it like a week later. I could still remember the facts. I remember the interesting things it brought in. It kind of brought the connection to the War of the Roses, which the first article didn\u2019t bring in, just because of how I selected kind of the options of where my curiosity led me, to me, it was very visceral how I tried to kind of learn the same thing from the Gemini, like a vanilla experience and guided learning. And one of them actually made me remember better without me even trying.<\/p>\n

Diane Tavenner: Interesting.<\/p>\n

Michael Horn: That\u2019s really cool. One really quick question on that. Like, what are the five learning science principles that you guys have prioritized to create that sort of experience? Just so we can enumerate it?<\/p>\n

Irina Jurenka: So the five learning science principles that we\u2019ve identified is to inspire active learning, to manage cognitive load, to adapt to the learner, stimulate curiosity and deepen metacognition. And we realize that this is not the comprehensive list. There are other important areas of learning science that we are considering to bring to kind of forefront of what we\u2019re optimizing for. But these are the first and the most important five learning science principles that we have been working towards so far.<\/p>\n

Diane Tavenner: Irina, one of the things that I like about talking to you about is that you talk about pedagogy and you said up front, you know, you had this hypothesis about tutors being sort of the way to go. I\u2019m curious about that because we\u2019ve also talked about, there\u2019s other kinds of ways to learn. And so I\u2019m curious if you guys are exploring other ways and how you think about that and why tutors and yeah, anything you can share around that?<\/p>\n

Irina Jurenka: Yes. I\u2019m actually curious to hear from you, given your experience, what you think would be exciting, other ways of learning for us to consider. The reason why we started with AI tutoring is because of, I guess this is where the strength of current GenAI model lies. It\u2019s kind of a text, chat based interface that we\u2019re all familiar with. So we thought, okay, how can we leverage what\u2019s already mature to make a difference in education? But we also realized that as new capabilities in AI are emerging and also maturing \u2014 for example \u2014 we have these demos of live experience where it\u2019s kind of video and audio and you essentially can just talk to AI in the same way as you would talk to your human teacher. We are also thinking about how to kind of, how to bring that to users in an interesting learning experience.<\/p>\n

But yeah, I would be very curious to hear from you what you think would be a good thing.<\/p>\n

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Related10 Useful Tech Tools for Educators in 2026: A Practical Guide<\/p>\n

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Learning: Content vs Skill Development<\/p>\n

Diane Tavenner: Well, I mean I think when I think about it, and it\u2019s hard to really parse how different this might be from a tutor, but I think about this type of learning more in kind of the factual content, vocabulary, the what I would call the content knowledge you need for learning. And then I often, you know, kind of crudely though separate from skill development. So how do you actually communicate effectively or write effectively or analyze problems? And you know, I historically have taken a project based or problem based approach to that. So you start with, kind of a big problem that you want to solve or a big question that you have and then you engage in a project that gets you an outcome or a product. And so that was pretty long winded. But maybe, maybe the most immediate would just be like, and maybe a tutor can do this, but really helping to teach someone to write effectively or communicate effectively. I think right now at least I, and I think other people are using it to just take what I\u2019ve written and write it better. But I\u2019m not sure that it\u2019s really teaching me yet, giving me that guided practice and that feedback and whatnot.<\/p>\n

So that might be the more near term version that I\u2019m thinking about.<\/p>\n

Irina Jurenka: Yeah. So first for the skills acquisition, we really hope that guided learning type experience could actually help with that. So your example of helping you rewrite a piece of text with guided learning, it won\u2019t just rewrite for you, it will guide you towards how to rewrite it so that you do it. So it will ask you to think about certain things, ask you certain questions. So hopefully a student can learn from just that experience. Another thing I mentioned earlier, that kind of metacognitive abilities are important to us to kind of make sure that the tutor optimizes those things as well. That\u2019s kind of another layer where hopefully a student will kind of be able to take a step back and understand, OK, how did I get to this? How did I rewrite this? What was important? How did I think about it so that next time they can actually almost like, guide themselves and won\u2019t need the tutor anymore.<\/p>\n

Diane Tavenner: That\u2019s so interesting. Last season, Michael and I interviewed the woman who leads the Harvard Writing center, and what you just described was her concern of what was not happening and what would be missed. And so it\u2019s interesting, the evolution, I think. I don\u2019t know, Michael, if you\u2019re \u2026<\/p>\n

Michael Horn: If you\u2019re tracking the same thing. Yeah, I think. I mean, I think it\u2019s interesting, right? And it\u2019s all a question of. I think. And this may be where you want to go, Diane, like, how do we put this in the hands of teachers and students, right, in productive ways so that they\u2019re not just jumping to the shortcut, but actually engaging in the difficult learning that you all are creating these experiences for?<\/p>\n

Diane Tavenner: I think that might be a good place for us to go and sort of, you know, bring this conversation, at least for now, to a conclusion. So, Irina, you are a new mother. And I know that when I became a mom, it changed how I viewed my work as an educator in ways that I couldn\u2019t even have imagined. And so, you know, I\u2019m curious, what, if anything, has changed for you in that. But even more so, what do you imagine your child\u2019s education will be like? You know, when you think about the next 5, 10, 15, 20 years, what will it look, the same? Will it look different? What do you want for it? What do you hope for it? You know, how do you think about that?<\/p>\n

AI, Change, and Human Connections<\/p>\n

Irina Jurenka: That is a great question. And I have this at the top of my mind. I think we are in a very unique situation, and kind of we\u2019re living through a very interesting period of time where the pace of change is so fast. I think even for us working in this industry, it\u2019s kind of head spinning, and it\u2019s even hard for us to catch up with all the progress. It\u2019s very hard to predict where AI will be in five to 10 years, what the role of education will be. We are actively thinking about this. I think what\u2019s becoming clear is the importance of human connections and building, kind of making sure that our next generation grows up as complete humans so that they\u2019re not just automatons who, you know, provide prompts to AI and just kind of live in this AI driven world where AI really is still a tool that helps human flourishing and helps prove and increase human connections. So I think for my child, I would want him to still go to school and to still have experiences learning how to communicate with his peers, how to talk to his teachers and be inspired by his teachers.<\/p>\n

I hope that AI can be something that helps him maybe learn faster and learn more and kind of really personalize his learning so that when he\u2019s really passionate about something, he can go off and go deeper with AI and maybe be able to do these projects that are not supported at school, but he can do at home with his peers. And AI can serve as kind of this facilitator and help them again achieve more interesting outcomes with their projects. But at the end of the day, I think I want him to have the breadth of experiences and knowledge and just learn how to be a good human.<\/p>\n

Diane Tavenner: That\u2019s a beautiful place, I think, to wrap.\u00a0<\/p>\n

Michael Horn: This season of Class Disrupted is sponsored by LearnerStudio, a nonprofit motivated by one question. What will young people need to be inspired and prepared to flourish in the age of AI as individuals, in careers, and for civil thriving? LearnerStudio is sponsoring this season on AI in education because in this critical moment, we need more than just hype. We need authentic conversations asking the right questions from a place of real curiosity and learning. You can learn more about LearnerStudio\u2019s mission and the innovators who inspire them at www.learnerstudio.org.<\/p>\n

Michael Horn: I was going to say we have this section, Irina, where we wrap up, where we share something that we\u2019ve been reading book wise or watching on TV or movie, podcasts, whatever. And so because we didn\u2019t prep you beforehand, we\u2019ll let Diane go first with hers and then we\u2019d love to hear what\u2019s on your. What\u2019s on your bedside table or in your ear or something like that. If you wouldn\u2019t mind sharing.<\/p>\n

Diane Tavenner: So, this is kind of a funny one. I\u2019m. I\u2019m listening to\/reading a book called The Five Types of Wealth: A Transformative Guide to Design Your Dream Life by Sahil Bloom. And if you\u2019re wondering about the types of wealth, according to Sahil, they are time, social, mental, physical and financial. And I\u2019m actually reading this with a group of other people who are sort of in our, depending on who you talk to, last half, last quarter, quarter of life. And we\u2019re exploring this question this year of how do I do my quote, best work in these chapters? So this is one of many things that we\u2019re using as a prompt to sort of create a rubric for ourselves, if you will, and self evaluate.<\/p>\n

And I\u2019m reading it now in prep for our next get together, so fascinating. I\u2019m not sure if I\u2019m like a wholehearted recommendation on it, but, you know, it\u2019s kind of, it includes a lot of the ideas that I think exist in a lot of other places and it\u2019s a good reminder.<\/p>\n

Michael Horn: Fair enough, fair enough. Well, it\u2019ll get marked down either way and we\u2019ll track it. Irina, what about you?<\/p>\n

Irina Jurenka: So I will be honest, I am struggling to find time to read given that I have a one-year-old, but I actually did manage to get through a book recently, and it was Neil Stevenson\u2019s novel Diamond Age. I\u2019ve been recommended it many, many times given the work I\u2019m doing. So I finally managed to read it. And so just if you haven\u2019t heard about it, it\u2019s about this world of the future where somebody designs essentially an AI tutor. So it\u2019s kind of this book that is given to a young girl and the book essentially teaches her everything throughout her life. And I think what\u2019s interesting, my takeaway from this was that there were three kinds of maybe original versions of the book that were given to three girls. And then they made, I guess, a copy that was given to everyone, which wasn\u2019t as good as. And the difference was that in the three original versions there was a human who was like essentially voicing out the text to the girls.<\/p>\n

And in the other versions it was like 100% AI. And what was interesting is that the human behind the book, even though they were just voicing what the text that the AI was producing according to the book, made a difference. Those three girls, especially the main character, who had this consistent one person who was guiding her throughout her whole life, actually built a connection with that person and grew up to be a much more successful, kind of, much better individual than anyone else. And it\u2019s this importance of still having a human in the loop.<\/p>\n

Michael Horn: Very cool. I love it. I\u2019ve heard a ton about that book, so I need to add it to my list I think now. I\u2019ll just say I\u2019m going to shock Diane here because we always make fun of me for not being current on stuff, but I actually not only did I watch seasons one and two of the Diplomat over the summer, season three came out and I\u2019m already done, so I\u2019m ahead. And so I am going to stay in my Netflix binging, I guess, at the moment, but I\u2019m feeling rather impressed with myself and that I got my Google sweatshirt from back when I lived in Silicon Valley on for this recording.<\/p>\n

So with that, Irina, I think Diane and I could both talk to you all day and just like learn from this. So really appreciate you joining and scratching the surface with us of all the things going on at DeepMind. And for all of you tuning in, we\u2019ll see you next time on Class Disrupted.<\/p>\n

This episode is sponsored by LearnerStudio.<\/p>\n

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