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One of the four pillars of good conversation is levity. You needn’t be a comedian, you can but have some funTetra Images, LLC/Alamy Conversation lies at the heart of our relationships – yet many of us find it surprisingly hard to talk to others. We may feel anxious at the thought of making small talk with strangers and struggle to connect with the people who are closest to us. If that sounds familiar, Alison Wood Brooks hopes to help. She is a professor at Harvard Business School, where she teaches an oversubscribed course called “TALK: How to talk gooder in business and life”, and the author of a new book, Talk: The science of conversation and the art of being ourselves. Both offer four key principles for more meaningful exchanges. Conversations are inherently unpredictable, says Wood Brooks, but they follow certain rules – and knowing their architecture makes us more comfortable with what is outside of our control. New Scientist asked her about the best ways to apply this research to our own chats. David Robson: Talking about talking feels quite meta. Do you ever find yourself critiquing your own performance? Alison Wood Brooks: There are so many levels of “meta-ness”. I have often felt like I’m floating over the room, watching conversations unfold, even as I’m involved in them myself. I teach a course at Harvard, and [my students] all get to experience this feeling as well. There can be an uncomfortable period of hypervigilance, but I hope that dissipates over time as they develop better habits. There is a famous quote from Charlie Parker, who was a jazz saxophonist. He said something like, “Practise, practise, practise, and then when you get on stage, let it all go and just wail.” I think that’s my approach to conversation. Even when you’re hyper-aware of conversation dynamics, you have to remember the true delight of being with another human mind, and never lose the magic of being together. Think ahead, but once you’re talking, let it all go and just wail. Reading your book, I learned that a good way to enliven a conversation is to ask someone why they are passionate about what they do. So, where does your passion for conversation come from? I have two answers to this question. One is professional. Early in my professorship at Harvard, I had been studying emotions by exploring how people talk about their feelings and the balance between what we feel inside and how we express that to others. And I realised I just had this deep, profound interest in figuring out how people talk to each other about everything, not just their feelings. We now have scientific tools that allow us to capture conversations and analyse them at large scale. Natural language processing, machine learning, the advent of AI – all this allows us to take huge swathes of transcript data and process it much more efficiently. Receive a weekly dose of discovery in your inbox. Sign up to newsletter The personal answer is that I’m an identical twin, and I spent my whole life, from the moment I opened my newborn eyes, existing next to a person who’s an exact copy of myself. It was like observing myself at very close range, interacting with the world, interacting with other people. I could see when she said and did things well, and I could try to do that myself. And I saw when her jokes failed, or she stumbled over her words – I tried to avoid those mistakes. It was a very fortunate form of feedback that not a lot of people get. And then, as a twin, you’ve got this person sharing a bedroom, sharing all your clothes, going to all the same parties and playing on the same sports teams, so we were just constantly in conversation with each other. You reached this level of shared reality that is so incredible, and I’ve spent the rest of my life trying to help other people get there in their relationships, too. “TALK” cleverly captures your framework for better conversations: topics, asking, levity and kindness. Let’s start at the beginning. How should we decide what to talk about? My first piece of advice is to prepare. Some people do this naturally. They already think about the things that they should talk about with somebody before they see them. They should lean into this habit. Some of my students, however, think it’s crazy. They think preparation will make the conversation seem rigid and forced and overly scripted. But just because you’ve thought ahead about what you might talk about doesn’t mean you have to talk about those things once the conversation is underway. It does mean, however, that you always have an idea waiting for you when you’re not sure what to talk about next. Having just one topic in your back pocket can help you in those anxiety-ridden moments. It makes things more fluent, which is important for establishing a connection. Choosing a topic is not only important at the start of a conversation. We’re constantly making decisions about whether we should stay on one subject, drift to something else or totally shift gears and go somewhere wildly different. Sometimes the topic of conversation is obvious. Even then, knowing when to switch to a new one can be trickyMartin Parr/Magnum Photos What’s your advice when making these decisions? There are three very clear signs that suggest that it’s time to switch topics. The first is longer mutual pauses. The second is more uncomfortable laughter, which we use to fill the space that we would usually fill excitedly with good content. And the third sign is redundancy. Once you start repeating things that have already been said on the topic, it’s a sign that you should move to something else. After an average conversation, most people feel like they’ve covered the right number of topics. But if you ask people after conversations that didn’t go well, they’ll more often say that they didn’t talk about enough things, rather than that they talked about too many things. This suggests that a common mistake is lingering too long on a topic after you’ve squeezed all the juice out of it. The second element of TALK is asking questions. I think a lot of us have heard the advice to ask more questions, yet many people don’t apply it. Why do you think that is? Many years of research have shown that the human mind is remarkably egocentric. Often, we are so focused on our own perspective that we forget to even ask someone else to share what’s in their mind. Another reason is fear. You’re interested in the other person, and you know you should ask them questions, but you’re afraid of being too intrusive, or that you will reveal your own incompetence, because you feel you should know the answer already. What kinds of questions should we be asking – and avoiding? In the book, I talk about the power of follow-up questions that build on anything that your partner has just said. It shows that you heard them, that you care and that you want to know more. Even one follow-up question can springboard us away from shallow talk into something deeper and more meaningful. There are, however, some bad patterns of question asking, such as “boomerasking”. Michael Yeomans [at Imperial College London] and I have a recent paper about this, and oh my gosh, it’s been such fun to study. It’s a play on the word boomerang: it comes back to the person who threw it. If I ask you what you had for breakfast, and you tell me you had Special K and banana, and then I say, “Well, let me tell you about my breakfast, because, boy, was it delicious” – that’s boomerasking. Sometimes it’s a thinly veiled way of bragging or complaining, but sometimes I think people are genuinely interested to hear from their partner, but then the partner’s answer reminds them so much of their own life that they can’t help but start sharing their perspective. In our research, we have found that this makes your partner feel like you weren’t interested in their perspective, so it seems very insincere. Sharing your own perspective is important. It’s okay at some point to bring the conversation back to yourself. But don’t do it so soon that it makes your partner feel like you didn’t hear their answer or care about it. Research by Alison Wood Brooks includes a recent study on “boomerasking”, a pitfall you should avoid to make conversations flowJanelle Bruno What are the benefits of levity? When we think of conversations that haven’t gone well, we often think of moments of hostility, anger or disagreement, but a quiet killer of conversation is boredom. Levity is the antidote. These small moments of sparkle or fizz can pull us back in and make us feel engaged with each other again. Our research has shown that we give status and respect to people who make us feel good, so much so that in a group of people, a person who can land even one appropriate joke is more likely to be voted as the leader. And the joke doesn’t even need to be very funny! It’s the fact that they were confident enough to try it and competent enough to read the room. Do you have any practical steps that people can apply to generate levity, even if they’re not a natural comedian? Levity is not just about being funny. In fact, aiming to be a comedian is not the right goal. When we watch stand-up on Netflix, comedians have rehearsed those jokes and honed them and practised them for a long time, and they’re delivering them in a monologue to an audience. It’s a completely different task from a live conversation. In real dialogue, what everybody is looking for is to feel engaged, and that doesn’t require particularly funny jokes or elaborate stories. When you see opportunities to make it fun or lighten the mood, that’s what you need to grab. It can come through a change to a new, fresh topic, or calling back to things that you talked about earlier in the conversation or earlier in your relationship. These callbacks – which sometimes do refer to something funny – are such a nice way of showing that you’ve listened and remembered. A levity move could also involve giving sincere compliments to other people. When you think nice things, when you admire someone, make sure you say it out loud. This brings us to the last element of TALK: kindness. Why do we so often fail to be as kind as we would like? Wobbles in kindness often come back to our egocentrism. Research shows that we underestimate how much other people’s perspectives differ from our own, and we forget that we have the tools to ask other people directly in conversation for their perspective. Being a kinder conversationalist is about trying to focus on your partner’s perspective and then figuring what they need and helping them to get it. Finally, what is your number one tip for readers to have a better conversation the next time they speak to someone? Every conversation is surprisingly tricky and complex. When things don’t go perfectly, give yourself and others more grace. There will be trips and stumbles and then a little grace can go very, very far. Topics:

Reading Time: 6 minutes What is marketing without data? Assumptions. Guesses. Fluff. For Chapter 6 of our book, “The Customer Engagement Book: Adapt or Die,” we spoke with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, to explore how engagement data can truly inform critical business decisions.  Alec discusses the different types of customer behaviors that matter most, how to separate meaningful information from the rest, and the role of systems that learn over time to create tailored customer experiences. This interview provides insights into using data for real-time actions and shaping the future of marketing. Prepare to learn about AI decision-making and how a focus on data is changing how we engage with customers.   Alec Haase Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? It’s a culmination of everything. Behavioral signals — the actual conversions and micro-conversions that users take within your product or website. Obviously, that’s things like purchases. But there are also other behavioral signals marketers should be using and thinking about. Things like micro-conversions — maybe that’s shopping for a product, clicking to learn more about a product, or visiting a certain page on your website. Behind that, you also need to have all your user data to tie that to. So I know someone took said action; I can follow up with them in email or out on paid social. I need the user identifiers to do that. 2. How do you distinguish between data that is actionable versus data that is just noise? Data that’s actionable includes the conversions and micro-conversions — very clear instances of “someone did this.” I can react to or measure those. What’s becoming a bit of a challenge for marketers is understanding that there’s other data that is valuable for machine learning or reinforcement learning models, things like tags on the types of products customers are interacting with. Maybe there’s category information about that product, or color information. That would otherwise look like noise to the average marketer. But behind the scenes, it can be used for reinforcement learning. There is definitely the “clear-cut” actionable data, but marketers shouldn’t be quick to classify things as noise because the rise in machine learning and reinforcement learning will make that data more valuable. 3. How can customer engagement data be used to identify and prioritize new business opportunities? At Hightouch, we don’t necessarily think about retroactive analysis. We have a system where we have customer engagement data firing in that we then have real-time scores reacting to. An interesting example is when you have machine learning and reinforcement learning models running. In the pet retailer example I gave you, the system is able to figure out what to prioritize. The concept of reinforcement learning is not a marketer making rules to say, “I know this type of thing works well on this type of audience.” It’s the machine itself using the data to determine what attribute responds well to which offer, recommendation, or marketing campaign. 4. How can marketers ensure their use of customer engagement data aligns with the broader business objectives? It starts with the objectives. It’s starting with the desired outcome and working your way back. That whole flip of the paradigm is starting with outcomes and letting the system optimize. What are you trying to drive, and then back into the types of experiences that can make that happen? There’s personalization. When we talk about data-driven experiences and personalization, Spotify Wrapped is the North Star. For Spotify Wrapped, you want to drive customer stickiness and create a brand. To make that happen, you want to send a personalized email. What components do you want in that email? Maybe it’s top five songs, top five artists, and then you can back into the actual event data you need to make that happen. 5. What role does engagement data play in influencing cross-functional decisions such as those in product development, sales, or customer service? For product development, it’s product analytics — knowing what features users are using, or seeing in heat maps where users are clicking. Sales is similar. We’re using behavioral signals like what types of content they’re reading on the site to help inform what they would be interested in — the types of products or the types of use cases. For customer service, you can look at errors they’ve run into in the past or specific purchases they’ve made, so that when you’re helping them the next time they engage with you, you know exactly what their past behaviors were and what products they could be calling about. 6. What are some challenges marketers face when trying to translate customer engagement data into actionable insights? Access to data is one challenge. You might not know what data you have because marketers historically may not have been used to the systems where data is stored. Historically, that’s been pretty siloed away from them. Rich behavioral data and other data across the business was stored somewhere else. Now, as more companies embrace the data warehouse at the center of their business, it gives everyone a true single place where data can be stored. Marketers are working more with data teams, understanding more about the data they have, and using that data to power downstream use cases, personalization, reinforcement learning, or general business insights. 7. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? As a marketer, I think proof is key. The best thing is if you’ve actually run a test. “I think we should do this. I ran a small test, and it’s showing that this is actually proving out.” Being able to clearly explain and justify your reasoning with data is super important. 8. What technology or tools have you found most effective for gathering and analyzing customer engagement data? Any type of behavioral event collection, specifically ones that write to the cloud data warehouse, is the critical component. Your data team is operating off the data warehouse. Having an event collection product that stores data in that central spot is really important if you want to use the other data when making recommendations. You want to get everything into the data warehouse where it can be used both for insights and for putting into action. For Spotify Wrapped, you want to collect behavioral event signals like songs listened to or concerts attended, writing to the warehouse so that you can get insights back — how many songs were played this year, projections for next month — but then you can also use those behavioral events in downstream platforms to fire off personalized emails with product recommendations or Spotify Wrapped-style experiences. 9. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? What we’re excited about is the concept of AI Decisioning — having AI agents actually using customer data to train their own models and decision-making to create personalized experiences. We’re sitting on top of all this behavioral data, engagement data, and user attributes, and our system is learning from all of that to make the best decisions across downstream systems. Whether that’s as simple as driving a loyalty program and figuring out what emails to send or what on-site experiences to show, or exposing insights that might lead you to completely change your business strategy, we see engagement data as the fuel to the engine of reinforcement learning, machine learning, AI agents, this whole next wave of Martech that’s just now coming. But it all starts with having the data to train those systems. I think that behavioral data is the fuel of modern Martech, and that only holds more true as Martech platforms adopt these decisioning and AI capabilities, because they’re only as good as the data that’s training the models.     This interview Q&A was hosted with Alec Haase, Product GTM Lead, Commerce and AI at Hightouch, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Alec Haase Q&A: Customer Engagement Book Interview appeared first on MoEngage.

Transcript [MUSIC]      [BOOK PASSAGE]   PETER LEE: “In ‘The Little Black Bag,’ a classic science fiction story, a high-tech doctor’s kit of the future is accidentally transported back to the 1950s, into the shaky hands of a washed-up, alcoholic doctor. The ultimate medical tool, it redeems the doctor wielding it, allowing him to practice gratifyingly heroic medicine. … The tale ends badly for the doctor and his treacherous assistant, but it offered a picture of how advanced technology could transform medicine—powerful when it was written nearly 75 years ago and still so today. What would be the Al equivalent of that little black bag? At this moment when new capabilities are emerging, how do we imagine them into medicine?”   [END OF BOOK PASSAGE]     [THEME MUSIC]     This is The AI Revolution in Medicine, Revisited. I’m your host, Peter Lee.    Shortly after OpenAI’s GPT-4 was publicly released, Carey Goldberg, Dr. Zak Kohane, and I published The AI Revolution in Medicine to help educate the world of healthcare and medical research about the transformative impact this new generative AI technology could have. But because we wrote the book when GPT-4 was still a secret, we had to speculate. Now, two years later, what did we get right, and what did we get wrong?     In this series, we’ll talk to clinicians, patients, hospital administrators, and others to understand the reality of AI in the field and where we go from here.   [THEME MUSIC FADES] The book passage I read at the top is from “Chapter 10: The Big Black Bag.”  In imagining AI in medicine, Carey, Zak, and I included in our book two fictional accounts. In the first, a medical resident consults GPT-4 on her personal phone as the patient in front of her crashes. Within seconds, it offers an alternate response based on recent literature. In the second account, a 90-year-old woman with several chronic conditions is living independently and receiving near-constant medical support from an AI aide.    In our conversations with the guests we’ve spoken to so far, we’ve caught a glimpse of these predicted futures, seeing how clinicians and patients are actually using AI today and how developers are leveraging the technology in the healthcare products and services they’re creating. In fact, that first fictional account isn’t so fictional after all, as most of the doctors in the real world actually appear to be using AI at least occasionally—and sometimes much more than occasionally—to help in their daily clinical work. And as for the second fictional account, which is more of a science fiction account, it seems we are indeed on the verge of a new way of delivering and receiving healthcare, though the future is still very much open.  As we continue to examine the current state of AI in healthcare and its potential to transform the field, I’m pleased to welcome Bill Gates and Sébastien Bubeck.   Bill may be best known as the co-founder of Microsoft, having created the company with his childhood friend Paul Allen in 1975. He’s now the founder of Breakthrough Energy, which aims to advance clean energy innovation, and TerraPower, a company developing groundbreaking nuclear energy and science technologies. He also chairs the world’s largest philanthropic organization, the Gates Foundation, and focuses on solving a variety of health challenges around the globe and here at home.  Sébastien is a research lead at OpenAI. He was previously a distinguished scientist, vice president of AI, and a colleague of mine here at Microsoft, where his work included spearheading the development of the family of small language models known as Phi. While at Microsoft, he also coauthored the discussion-provoking 2023 paper “Sparks of Artificial General Intelligence,” which presented the results of early experiments with GPT-4 conducted by a small team from Microsoft Research.    [TRANSITION MUSIC]   Here’s my conversation with Bill Gates and Sébastien Bubeck.  LEE: Bill, welcome.  BILL GATES: Thank you.  LEE: Seb …  SÉBASTIEN BUBECK: Yeah. Hi, hi, Peter. Nice to be here.  LEE: You know, one of the things that I’ve been doing just to get the conversation warmed up is to talk about origin stories, and what I mean about origin stories is, you know, what was the first contact that you had with large language models or the concept of generative AI that convinced you or made you think that something really important was happening?  And so, Bill, I think I’ve heard the story about, you know, the time when the OpenAI folks—Sam Altman, Greg Brockman, and others—showed you something, but could we hear from you what those early encounters were like and what was going through your mind?   GATES: Well, I’d been visiting OpenAI soon after it was created to see things like GPT-2 and to see the little arm they had that was trying to match human manipulation and, you know, looking at their games like Dota that they were trying to get as good as human play. And honestly, I didn’t think the language model stuff they were doing, even when they got to GPT-3, would show the ability to learn, you know, in the same sense that a human reads a biology book and is able to take that knowledge and access it not only to pass a test but also to create new medicines.  And so my challenge to them was that if their LLM could get a five on the advanced placement biology test, then I would say, OK, it took biologic knowledge and encoded it in an accessible way and that I didn’t expect them to do that very quickly but it would be profound.   And it was only about six months after I challenged them to do that, that an early version of GPT-4 they brought up to a dinner at my house, and in fact, it answered most of the questions that night very well. The one it got totally wrong, we were … because it was so good, we kept thinking, Oh, we must be wrong. It turned out it was a math weakness [LAUGHTER] that, you know, we later understood that that was an area of, weirdly, of incredible weakness of those early models. But, you know, that was when I realized, OK, the age of cheap intelligence was at its beginning.  LEE: Yeah. So I guess it seems like you had something similar to me in that my first encounters, I actually harbored some skepticism. Is it fair to say you were skeptical before that?  GATES: Well, the idea that we’ve figured out how to encode and access knowledge in this very deep sense without even understanding the nature of the encoding, …  LEE: Right.   GATES: … that is a bit weird.   LEE: Yeah.  GATES: We have an algorithm that creates the computation, but even say, OK, where is the president’s birthday stored in there? Where is this fact stored in there? The fact that even now when we’re playing around, getting a little bit more sense of it, it’s opaque to us what the semantic encoding is, it’s, kind of, amazing to me. I thought the invention of knowledge storage would be an explicit way of encoding knowledge, not an implicit statistical training.  LEE: Yeah, yeah. All right. So, Seb, you know, on this same topic, you know, I got—as we say at Microsoft—I got pulled into the tent. [LAUGHS]  BUBECK: Yes.   LEE: Because this was a very secret project. And then, um, I had the opportunity to select a small number of researchers in MSR [Microsoft Research] to join and start investigating this thing seriously. And the first person I pulled in was you.  BUBECK: Yeah.  LEE: And so what were your first encounters? Because I actually don’t remember what happened then.  BUBECK: Oh, I remember it very well. [LAUGHS] My first encounter with GPT-4 was in a meeting with the two of you, actually. But my kind of first contact, the first moment where I realized that something was happening with generative AI, was before that. And I agree with Bill that I also wasn’t too impressed by GPT-3.  I though that it was kind of, you know, very naturally mimicking the web, sort of parroting what was written there in a nice way. Still in a way which seemed very impressive. But it wasn’t really intelligent in any way. But shortly after GPT-3, there was a model before GPT-4 that really shocked me, and this was the first image generation model, DALL-E 1.  So that was in 2021. And I will forever remember the press release of OpenAI where they had this prompt of an avocado chair and then you had this image of the avocado chair. [LAUGHTER] And what really shocked me is that clearly the model kind of “understood” what is a chair, what is an avocado, and was able to merge those concepts.  So this was really, to me, the first moment where I saw some understanding in those models.   LEE: So this was, just to get the timing right, that was before I pulled you into the tent.  BUBECK: That was before. That was like a year before.  LEE: Right.   BUBECK: And now I will tell you how, you know, we went from that moment to the meeting with the two of you and GPT-4.  So once I saw this kind of understanding, I thought, OK, fine. It understands concept, but it’s still not able to reason. It cannot—as, you know, Bill was saying—it cannot learn from your document. It cannot reason.   So I set out to try to prove that. You know, this is what I was in the business of at the time, trying to prove things in mathematics. So I was trying to prove that basically autoregressive transformers could never reason. So I was trying to prove this. And after a year of work, I had something reasonable to show. And so I had the meeting with the two of you, and I had this example where I wanted to say, there is no way that an LLM is going to be able to do x.  And then as soon as I … I don’t know if you remember, Bill. But as soon as I said that, you said, oh, but wait a second. I had, you know, the OpenAI crew at my house recently, and they showed me a new model. Why don’t we ask this new model this question?   LEE: Yeah. BUBECK: And we did, and it solved it on the spot. And that really, honestly, just changed my life. Like, you know, I had been working for a year trying to say that this was impossible. And just right there, it was shown to be possible.   LEE: [LAUGHS] One of the very first things I got interested in—because I was really thinking a lot about healthcare—was healthcare and medicine.  And I don’t know if the two of you remember, but I ended up doing a lot of tests. I ran through, you know, step one and step two of the US Medical Licensing Exam. Did a whole bunch of other things. I wrote this big report. It was, you know, I can’t remember … a couple hundred pages.   And I needed to share this with someone. I didn’t … there weren’t too many people I could share it with. So I sent, I think, a copy to you, Bill. Sent a copy to you, Seb.   I hardly slept for about a week putting that report together. And, yeah, and I kept working on it. But I was far from alone. I think everyone who was in the tent, so to speak, in those early days was going through something pretty similar. All right. So I think … of course, a lot of what I put in the report also ended up being examples that made it into the book.  But the main purpose of this conversation isn’t to reminisce about [LAUGHS] or indulge in those reminiscences but to talk about what’s happening in healthcare and medicine. And, you know, as I said, we wrote this book. We did it very, very quickly. Seb, you helped. Bill, you know, you provided a review and some endorsements.  But, you know, honestly, we didn’t know what we were talking about because no one had access to this thing. And so we just made a bunch of guesses. So really, the whole thing I wanted to probe with the two of you is, now with two years of experience out in the world, what, you know, what do we think is happening today?  You know, is AI actually having an impact, positive or negative, on healthcare and medicine? And what do we now think is going to happen in the next two years, five years, or 10 years? And so I realize it’s a little bit too abstract to just ask it that way. So let me just try to narrow the discussion and guide us a little bit.   Um, the kind of administrative and clerical work, paperwork, around healthcare—and we made a lot of guesses about that—that appears to be going well, but, you know, Bill, I know we’ve discussed that sometimes that you think there ought to be a lot more going on. Do you have a viewpoint on how AI is actually finding its way into reducing paperwork?  GATES: Well, I’m stunned … I don’t think there should be a patient-doctor meeting where the AI is not sitting in and both transcribing, offering to help with the paperwork, and even making suggestions, although the doctor will be the one, you know, who makes the final decision about the diagnosis and whatever prescription gets done.   It’s so helpful. You know, when that patient goes home and their, you know, son who wants to understand what happened has some questions, that AI should be available to continue that conversation. And the way you can improve that experience and streamline things and, you know, involve the people who advise you. I don’t understand why that’s not more adopted, because there you still have the human in the loop making that final decision.  But even for, like, follow-up calls to make sure the patient did things, to understand if they have concerns and knowing when to escalate back to the doctor, the benefit is incredible. And, you know, that thing is ready for prime time. That paradigm is ready for prime time, in my view.  LEE: Yeah, there are some good products, but it seems like the number one use right now—and we kind of got this from some of the previous guests in previous episodes—is the use of AI just to respond to emails from patients. [LAUGHTER] Does that make sense to you?  BUBECK: Yeah. So maybe I want to second what Bill was saying but maybe take a step back first. You know, two years ago, like, the concept of clinical scribes, which is one of the things that we’re talking about right now, it would have sounded, in fact, it sounded two years ago, borderline dangerous. Because everybody was worried about hallucinations. What happened if you have this AI listening in and then it transcribes, you know, something wrong?  Now, two years later, I think it’s mostly working. And in fact, it is not yet, you know, fully adopted. You’re right. But it is in production. It is used, you know, in many, many places. So this rate of progress is astounding because it wasn’t obvious that we would be able to overcome those obstacles of hallucination. It’s not to say that hallucinations are fully solved. In the case of the closed system, they are.   Now, I think more generally what’s going on in the background is that there is something that we, that certainly I, underestimated, which is this management overhead. So I think the reason why this is not adopted everywhere is really a training and teaching aspect. People need to be taught, like, those systems, how to interact with them.  And one example that I really like, a study that recently appeared where they tried to use ChatGPT for diagnosis and they were comparing doctors without and with ChatGPT (opens in new tab). And the amazing thing … so this was a set of cases where the accuracy of the doctors alone was around 75%. ChatGPT alone was 90%. So that’s already kind of mind blowing. But then the kicker is that doctors with ChatGPT was 80%.   Intelligence alone is not enough. It’s also how it’s presented, how you interact with it. And ChatGPT, it’s an amazing tool. Obviously, I absolutely love it. But it’s not … you don’t want a doctor to have to type in, you know, prompts and use it that way.  It should be, as Bill was saying, kind of running continuously in the background, sending you notifications. And you have to be really careful of the rate at which those notifications are being sent. Because if they are too frequent, then the doctor will learn to ignore them. So you have to … all of those things matter, in fact, at least as much as the level of intelligence of the machine.  LEE: One of the things I think about, Bill, in that scenario that you described, doctors do some thinking about the patient when they write the note. So, you know, I’m always a little uncertain whether it’s actually … you know, you wouldn’t necessarily want to fully automate this, I don’t think. Or at least there needs to be some prompt to the doctor to make sure that the doctor puts some thought into what happened in the encounter with the patient. Does that make sense to you at all?  GATES: At this stage, you know, I’d still put the onus on the doctor to write the conclusions and the summary and not delegate that.  The tradeoffs you make a little bit are somewhat dependent on the situation you’re in. If you’re in Africa, So, yes, the doctor’s still going to have to do a lot of work, but just the quality of letting the patient and the people around them interact and ask questions and have things explained, that alone is such a quality improvement. It’s mind blowing.   LEE: So since you mentioned, you know, Africa—and, of course, this touches on the mission and some of the priorities of the Gates Foundation and this idea of democratization of access to expert medical care—what’s the most interesting stuff going on right now? Are there people and organizations or technologies that are impressing you or that you’re tracking?  GATES: Yeah. So the Gates Foundation has given out a lot of grants to people in Africa doing education, agriculture but more healthcare examples than anything. And the way these things start off, they often start out either being patient-centric in a narrow situation, like, OK, I’m a pregnant woman; talk to me. Or, I have infectious disease symptoms; talk to me. Or they’re connected to a health worker where they’re helping that worker get their job done. And we have lots of pilots out, you know, in both of those cases.   The dream would be eventually to have the thing the patient consults be so broad that it’s like having a doctor available who understands the local things.   LEE: Right.   GATES: We’re not there yet. But over the next two or three years, you know, particularly given the worsening financial constraints against African health systems, where the withdrawal of money has been dramatic, you know, figuring out how to take this—what I sometimes call “free intelligence”—and build a quality health system around that, we will have to be more radical in low-income countries than any rich country is ever going to be.   LEE: Also, there’s maybe a different regulatory environment, so some of those things maybe are easier? Because right now, I think the world hasn’t figured out how to and whether to regulate, let’s say, an AI that might give a medical diagnosis or write a prescription for a medication.  BUBECK: Yeah. I think one issue with this, and it’s also slowing down the deployment of AI in healthcare more generally, is a lack of proper benchmark. Because, you know, you were mentioning the USMLE [United States Medical Licensing Examination], for example. That’s a great test to test human beings and their knowledge of healthcare and medicine. But it’s not a great test to give to an AI.  It’s not asking the right questions. So finding what are the right questions to test whether an AI system is ready to give diagnosis in a constrained setting, that’s a very, very important direction, which to my surprise, is not yet accelerating at the rate that I was hoping for.  LEE: OK, so that gives me an excuse to get more now into the core AI tech because something I’ve discussed with both of you is this issue of what are the right tests. And you both know the very first test I give to any new spin of an LLM is I present a patient, the results—a mythical patient—the results of my physical exam, my mythical physical exam. Maybe some results of some initial labs. And then I present or propose a differential diagnosis. And if you’re not in medicine, a differential diagnosis you can just think of as a prioritized list of the possible diagnoses that fit with all that data. And in that proposed differential, I always intentionally make two mistakes.  I make a textbook technical error in one of the possible elements of the differential diagnosis, and I have an error of omission. And, you know, I just want to know, does the LLM understand what I’m talking about? And all the good ones out there do now. But then I want to know, can it spot the errors? And then most importantly, is it willing to tell me I’m wrong, that I’ve made a mistake?   That last piece seems really hard for AI today. And so let me ask you first, Seb, because at the time of this taping, of course, there was a new spin of GPT-4o last week that became overly sycophantic. In other words, it was actually prone in that test of mine not only to not tell me I’m wrong, but it actually praised me for the creativity of my differential. [LAUGHTER] What’s up with that?  BUBECK: Yeah, I guess it’s a testament to the fact that training those models is still more of an art than a science. So it’s a difficult job. Just to be clear with the audience, we have rolled back that [LAUGHS] version of GPT-4o, so now we don’t have the sycophant version out there.  Yeah, no, it’s a really difficult question. It has to do … as you said, it’s very technical. It has to do with the post-training and how, like, where do you nudge the model? So, you know, there is this very classical by now technique called RLHF [reinforcement learning from human feedback], where you push the model in the direction of a certain reward model. So the reward model is just telling the model, you know, what behavior is good, what behavior is bad.  But this reward model is itself an LLM, and, you know, Bill was saying at the very beginning of the conversation that we don’t really understand how those LLMs deal with concepts like, you know, where is the capital of France located? Things like that. It is the same thing for this reward model. We don’t know why it says that it prefers one output to another, and whether this is correlated with some sycophancy is, you know, something that we discovered basically just now. That if you push too hard in optimization on this reward model, you will get a sycophant model.  So it’s kind of … what I’m trying to say is we became too good at what we were doing, and we ended up, in fact, in a trap of the reward model.  LEE: I mean, you do want … it’s a difficult balance because you do want models to follow your desires and …  BUBECK: It’s a very difficult, very difficult balance.  LEE: So this brings up then the following question for me, which is the extent to which we think we’ll need to have specially trained models for things. So let me start with you, Bill. Do you have a point of view on whether we will need to, you know, quote-unquote take AI models to med school? Have them specially trained? Like, if you were going to deploy something to give medical care in underserved parts of the world, do we need to do something special to create those models?  GATES: We certainly need to teach them the African languages and the unique dialects so that the multimedia interactions are very high quality. We certainly need to teach them the disease prevalence and unique disease patterns like, you know, neglected tropical diseases and malaria. So we need to gather a set of facts that somebody trying to go for a US customer base, you know, wouldn’t necessarily have that in there.  Those two things are actually very straightforward because the additional training time is small. I’d say for the next few years, we’ll also need to do reinforcement learning about the context of being a doctor and how important certain behaviors are. Humans learn over the course of their life to some degree that, I’m in a different context and the way I behave in terms of being willing to criticize or be nice, you know, how important is it? Who’s here? What’s my relationship to them?   Right now, these machines don’t have that broad social experience. And so if you know it’s going to be used for health things, a lot of reinforcement learning of the very best humans in that context would still be valuable. Eventually, the models will, having read all the literature of the world about good doctors, bad doctors, it’ll understand as soon as you say, “I want you to be a doctor diagnosing somebody.” All of the implicit reinforcement that fits that situation, you know, will be there. LEE: Yeah. GATES: And so I hope three years from now, we don’t have to do that reinforcement learning. But today, for any medical context, you would want a lot of data to reinforce tone, willingness to say things when, you know, there might be something significant at stake.  LEE: Yeah. So, you know, something Bill said, kind of, reminds me of another thing that I think we missed, which is, the context also … and the specialization also pertains to different, I guess, what we still call “modes,” although I don’t know if the idea of multimodal is the same as it was two years ago. But, you know, what do you make of all of the hubbub around—in fact, within Microsoft Research, this is a big deal, but I think we’re far from alone—you know, medical images and vision, video, proteins and molecules, cell, you know, cellular data and so on.  BUBECK: Yeah. OK. So there is a lot to say to everything … to the last, you know, couple of minutes. Maybe on the specialization aspect, you know, I think there is, hiding behind this, a really fundamental scientific question of whether eventually we have a singular AGI [artificial general intelligence] that kind of knows everything and you can just put, you know, explain your own context and it will just get it and understand everything.  That’s one vision. I have to say, I don’t particularly believe in this vision. In fact, we humans are not like that at all. I think, hopefully, we are general intelligences, yet we have to specialize a lot. And, you know, I did myself a lot of RL, reinforcement learning, on mathematics. Like, that’s what I did, you know, spent a lot of time doing that. And I didn’t improve on other aspects. You know, in fact, I probably degraded in other aspects. [LAUGHTER] So it’s … I think it’s an important example to have in mind.  LEE: I think I might disagree with you on that, though, because, like, doesn’t a model have to see both good science and bad science in order to be able to gain the ability to discern between the two?  BUBECK: Yeah, no, that absolutely. I think there is value in seeing the generality, in having a very broad base. But then you, kind of, specialize on verticals. And this is where also, you know, open-weights model, which we haven’t talked about yet, are really important because they allow you to provide this broad base to everyone. And then you can specialize on top of it.  LEE: So we have about three hours of stuff to talk about, but our time is actually running low. BUBECK: Yes, yes, yes.   LEE: So I think I want … there’s a more provocative question. It’s almost a silly question, but I need to ask it of the two of you, which is, is there a future, you know, where AI replaces doctors or replaces, you know, medical specialties that we have today? So what does the world look like, say, five years from now?  GATES: Well, it’s important to distinguish healthcare discovery activity from healthcare delivery activity. We focused mostly on delivery. I think it’s very much within the realm of possibility that the AI is not only accelerating healthcare discovery but substituting for a lot of the roles of, you know, I’m an organic chemist, or I run various types of assays. I can see those, which are, you know, testable-output-type jobs but with still very high value, I can see, you know, some replacement in those areas before the doctor.   The doctor, still understanding the human condition and long-term dialogues, you know, they’ve had a lifetime of reinforcement of that, particularly when you get into areas like mental health. So I wouldn’t say in five years, either people will choose to adopt it, but it will be profound that there’ll be this nearly free intelligence that can do follow-up, that can help you, you know, make sure you went through different possibilities.  And so I’d say, yes, we’ll have doctors, but I’d say healthcare will be massively transformed in its quality and in efficiency by AI in that time period.  LEE: Is there a comparison, useful comparison, say, between doctors and, say, programmers, computer programmers, or doctors and, I don’t know, lawyers?  GATES: Programming is another one that has, kind of, a mathematical correctness to it, you know, and so the objective function that you’re trying to reinforce to, as soon as you can understand the state machines, you can have something that’s “checkable”; that’s correct. So I think programming, you know, which is weird to say, that the machine will beat us at most programming tasks before we let it take over roles that have deep empathy, you know, physical presence and social understanding in them.  LEE: Yeah. By the way, you know, I fully expect in five years that AI will produce mathematical proofs that are checkable for validity, easily checkable, because they’ll be written in a proof-checking language like Lean or something but will be so complex that no human mathematician can understand them. I expect that to happen.   I can imagine in some fields, like cellular biology, we could have the same situation in the future because the molecular pathways, the chemistry, biochemistry of human cells or living cells is as complex as any mathematics, and so it seems possible that we may be in a state where in wet lab, we see, Oh yeah, this actually works, but no one can understand why.  BUBECK: Yeah, absolutely. I mean, I think I really agree with Bill’s distinction of the discovery and the delivery, and indeed, the discovery’s when you can check things, and at the end, there is an artifact that you can verify. You know, you can run the protocol in the wet lab and see [if you have] produced what you wanted. So I absolutely agree with that.   And in fact, you know, we don’t have to talk five years from now. I don’t know if you know, but just recently, there was a paper that was published on a scientific discovery using o3- mini (opens in new tab). So this is really amazing. And, you know, just very quickly, just so people know, it was about this statistical physics model, the frustrated Potts model, which has to do with coloring, and basically, the case of three colors, like, more than two colors was open for a long time, and o3 was able to reduce the case of three colors to two colors.   LEE: Yeah.  BUBECK: Which is just, like, astounding. And this is not … this is now. This is happening right now. So this is something that I personally didn’t expect it would happen so quickly, and it’s due to those reasoning models.   Now, on the delivery side, I would add something more to it for the reason why doctors and, in fact, lawyers and coders will remain for a long time, and it’s because we still don’t understand how those models generalize. Like, at the end of the day, we are not able to tell you when they are confronted with a really new, novel situation, whether they will work or not.  Nobody is able to give you that guarantee. And I think until we understand this generalization better, we’re not going to be willing to just let the system in the wild without human supervision.  LEE: But don’t human doctors, human specialists … so, for example, a cardiologist sees a patient in a certain way that a nephrologist …  BUBECK: Yeah. LEE: … or an endocrinologist might not. BUBECK: That’s right. But another cardiologist will understand and, kind of, expect a certain level of generalization from their peer. And this, we just don’t have it with AI models. Now, of course, you’re exactly right. That generalization is also hard for humans. Like, if you have a human trained for one task and you put them into another task, then you don’t … you often don’t know. LEE: OK. You know, the podcast is focused on what’s happened over the last two years. But now, I’d like one provocative prediction about what you think the world of AI and medicine is going to be at some point in the future. You pick your timeframe. I don’t care if it’s two years or 20 years from now, but, you know, what do you think will be different about AI in medicine in that future than today?  BUBECK: Yeah, I think the deployment is going to accelerate soon. Like, we’re really not missing very much. There is this enormous capability overhang. Like, even if progress completely stopped, with current systems, we can do a lot more than what we’re doing right now. So I think this will … this has to be realized, you know, sooner rather than later.  And I think it’s probably dependent on these benchmarks and proper evaluation and tying this with regulation. So these are things that take time in human society and for good reason. But now we already are at two years; you know, give it another two years and it should be really …   LEE: Will AI prescribe your medicines? Write your prescriptions?  BUBECK: I think yes. I think yes.  LEE: OK. Bill?  GATES: Well, I think the next two years, we’ll have massive pilots, and so the amount of use of the AI, still in a copilot-type mode, you know, we should get millions of patient visits, you know, both in general medicine and in the mental health side, as well. And I think that’s going to build up both the data and the confidence to give the AI some additional autonomy. You know, are you going to let it talk to you at night when you’re panicked about your mental health with some ability to escalate? And, you know, I’ve gone so far as to tell politicians with national health systems that if they deploy AI appropriately, that the quality of care, the overload of the doctors, the improvement in the economics will be enough that their voters will be stunned because they just don’t expect this, and, you know, they could be reelected [LAUGHTER] just on this one thing of fixing what is a very overloaded and economically challenged health system in these rich countries.  You know, my personal role is going to be to make sure that in the poorer countries, there isn’t some lag; in fact, in many cases, that we’ll be more aggressive because, you know, we’re comparing to having no access to doctors at all. And, you know, so I think whether it’s India or Africa, there’ll be lessons that are globally valuable because we need medical intelligence. And, you know, thank god AI is going to provide a lot of that.  LEE: Well, on that optimistic note, I think that’s a good way to end. Bill, Seb, really appreciate all of this.   I think the most fundamental prediction we made in the book is that AI would actually find its way into the practice of medicine, and I think that that at least has come true, maybe in different ways than we expected, but it’s come true, and I think it’ll only accelerate from here. So thanks again, both of you.  [TRANSITION MUSIC]  GATES: Yeah. Thanks, you guys.  BUBECK: Thank you, Peter. Thanks, Bill.  LEE: I just always feel such a sense of privilege to have a chance to interact and actually work with people like Bill and Sébastien.    With Bill, I’m always amazed at how practically minded he is. He’s really thinking about the nuts and bolts of what AI might be able to do for people, and his thoughts about underserved parts of the world, the idea that we might actually be able to empower people with access to expert medical knowledge, I think is both inspiring and amazing.   And then, Seb, Sébastien Bubeck, he’s just absolutely a brilliant mind. He has a really firm grip on the deep mathematics of artificial intelligence and brings that to bear in his research and development work. And where that mathematics takes him isn’t just into the nuts and bolts of algorithms but into philosophical questions about the nature of intelligence.   One of the things that Sébastien brought up was the state of evaluation of AI systems. And indeed, he was fairly critical in our conversation. But of course, the world of AI research and development is just moving so fast, and indeed, since we recorded our conversation, OpenAI, in fact, released a new evaluation metric that is directly relevant to medical applications, and that is something called HealthBench. And Microsoft Research also released a new evaluation approach or process called ADeLe.   HealthBench and ADeLe are examples of new approaches to evaluating AI models that are less about testing their knowledge and ability to pass multiple-choice exams and instead are evaluation approaches designed to assess how well AI models are able to complete tasks that actually arise every day in typical healthcare or biomedical research settings. These are examples of really important good work that speak to how well AI models work in the real world of healthcare and biomedical research and how well they can collaborate with human beings in those settings.  You know, I asked Bill and Seb to make some predictions about the future. You know, my own answer, I expect that we’re going to be able to use AI to change how we diagnose patients, change how we decide treatment options.   If you’re a doctor or a nurse and you encounter a patient, you’ll ask questions, do a physical exam, you know, call out for labs just like you do today, but then you’ll be able to engage with AI based on all of that data and just ask, you know, based on all the other people who have gone through the same experience, who have similar data, how were they diagnosed? How were they treated? What were their outcomes? And what does that mean for the patient I have right now? Some people call it the “patients like me” paradigm. And I think that’s going to become real because of AI within our lifetimes. That idea of really grounding the delivery in healthcare and medical practice through data and intelligence, I actually now don’t see any barriers to that future becoming real.  [THEME MUSIC]  I’d like to extend another big thank you to Bill and Sébastien for their time. And to our listeners, as always, it’s a pleasure to have you along for the ride. I hope you’ll join us for our remaining conversations, as well as a second coauthor roundtable with Carey and Zak.   Until next time.   [MUSIC FADES]

Greg Touhill, director of the Software Engineering’s Institute’s (SEI’s) Computer Emergency Response Team (CERT) division is an atypical technology leader. For one thing, he’s been in tech and other leadership positions that span the US Air Force, the US government, the private sector and now SEI’s CERT. More importantly, he’s been a major force in the cybersecurity realm, making the world a safer place and even saving lives. Touhill earned a bachelor’s degree from the Pennsylvania State University, a master’s degree from the University of Southern California, a master’s degree from the Air War College, was a senior executive fellow at the Harvard University Kennedy School of Government and completed executive education studies at the University of North Carolina. “I was a student intern at Carnegie Mellon, but I was going to college at Penn State and studying chemical engineering. As an Air Force ROTC scholarship recipient, I knew I was going to become an Air Force officer but soon realized that I didn’t necessarily want to be a chemical engineer in the Air Force,” says Touhill. “Because I passed all the mathematics, physics, and engineering courses, I ended up becoming a communications, electronics, and computer systems officer in the Air Force. I spent 30 years, one month and three days on active duty in the United States Air Force, eventually retiring as a brigadier general and having done many different types of jobs that were available to me within and even beyond my career field.” Related:Specifically, he was an operational commander at the squadron, group, and wing levels. For example, as a colonel, Touhill served as director of command, control, communications and computers (C4) for the United States Central Command Forces, then he was appointed chief information officer and director, communications and information at Air Mobility Command. Later, he served as commander, 81st Training Wing at Kessler Air Force Base where he was promoted to brigadier general and commanded over 12,500 personnel. After that, he served as the senior defense officer and US defense attaché at the US Embassy in Kuwait, before concluding his military career as the chief information officer and director, C4 systems at the US Transportation Command, one of 10 US combatant commands, where he and his team were awarded the NSA Rowlett Award for the best cybersecurity program in the government. While in the Air Force, Touhill received numerous awards and decorations including the Bronze Star medal and the Air Force Science and Engineering Award. He is the only three-time recipient of the USAF C4 Professionalism Award. Related:Greg Touhill“I got to serve at major combatant commands, work with coalition partners from many different countries and represented the US as part of a diplomatic mission to Kuwait for two years as the senior defense official at a time when America was withdrawing forces out of Iraq. I also led the negotiation of a new bilateral defense agreement with the Kuwaitis,” says Touhill. “Then I was recruited to continue my service and was asked to serve as the deputy assistant secretary of cybersecurity and communications at the Department of Homeland Security, where I ran the operations of what is now known as the Cybersecurity and Infrastructure Security Agency. I was there at a pivotal moment because we were building up the capacity of that organization and setting the stage for it to become its own agency.” While at DHS, there were many noteworthy breaches including the infamous US Office of People Management (OPM) breach. Those events led to Obama’s visit to the National Cybersecurity and Communications Integration Center.  “I got to brief the president on the state of cybersecurity, what we had seen with the OPM breach and some other deficiencies,” says Touhill. “I was on the federal CIO council as the cybersecurity advisor to that since I’d been a federal CIO before and I got to conclude my federal career by being the first United States government chief information security officer. From there, I pivoted to industry, but I also got to return to Carnegie Mellon as a faculty member at Carnegie Mellon’s Heinz College, where I've been teaching since January 2017.” Related:Touhill has been involved in three startups, two of which were successfully acquired. He also served on three Fortune 100 advisory boards and on the Information Systems Audit and Control Association board, eventually becoming its chair for a term during the seven years he served there. Touhill just celebrated his fourth year at CERT, which he considers the pinnacle of the cybersecurity profession and everything he’s done to date. “Over my career I've led teams that have done major software builds in the national security space. I've also been the guy who's pulled cables and set up routers, hubs and switches, and I've been a system administrator. I've done everything that I could do from the keyboard up all the way up to the White House,” says Touhill. “For 40 years, the Software Engineering Institute has been leading the world in secure by design, cybersecurity, software engineering, artificial intelligence and engineering, pioneering best practices, and figuring out how to make the world a safer more secure and trustworthy place. I’ve had a hand in the making of today’s modern military and government information technology environment, beginning as a 22-year-old lieutenant, and hope to inspire the next generation to do even better.” What ‘Success’ Means Many people would be satisfied with their careers as a brigadier general, a tech leader, the White House’s first anything, or working at CERT, let alone running it. Touhill has spent his entire career making the world a safer place, so it’s not surprising that he considers his greatest achievement saving lives. “In the Middle East and Iraq, convoys were being attacked with improvised explosive devices. There were also ‘direct fire’ attacks where people are firing weapons at you and indirect fire attacks where you could be in the line of fire,” says Touhill. “The convoys were using SINCGARS line-of-site walkie-talkies for communications that are most effective when the ground is flat, and Iraq is not flat. As a result, our troops were at risk of not having reliable communications while under attack. As my team brainstormed options to remedy the situation, one of my guys found some technology, about the size of an iPhone, that could covert a radio signal, which is basically a waveform, into a digital pulse I could put on a dedicated network to support the convoy missions.” For $11 million, Touhill and his team quickly architected, tested, and fielded the Radio over IP network (aka “Ripper Net”) that had a 99% reliability rate anywhere in Iraq. Better still, convoys could communicate over the network using any radios. That solution saved a minimum of six lives. In one case, the hospital doctor said if the patient had arrived five minutes later, he would have died. Sage Advice Anyone who has ever spent time in the military or in a military family knows that soldiers are very well disciplined, or they wash out. Other traits include being physically fit, mentally fit, and achieving balance in life, though that’s difficult to achieve in combat. Still, it’s a necessity. “I served three and a half years down range in combat operations. My experience taught me you could be doing 20-hour days for a year or two on end. If you haven’t built a good foundation of being disciplined and fit, it impacts your ability to maintain presence in times of stress, and CISOs work in stressful situations,” says Touhill. “Staying fit also fortifies you for the long haul, so you don’t get burned out as fast.” Another necessary skill is the ability to work well with others.  “Cybersecurity is an interdisciplinary practice. One of the great joys I have as CERT director is the wide range of experts in many different fields that include software engineers, computer engineers, computer scientists, data scientists, mathematicians and physicists,” says Touhill. “I have folks who have business degrees and others who have philosophy degrees. It's really a rich community of interests all coming together towards that common goal of making the world a safer, more secure and more trusted place in the cyber domain. We’re are kind of like the cyber neighborhood watch for the whole world.” He also says that money isn’t everything, having taken a pay cut to go from being an Air Force brigadier general to the deputy assistant secretary of the Department of Homeland Security . “You’ll always do well if you pick the job that matters most. That’s what I did, and I’ve been rewarded every step,” says Touhill.  The biggest challenge he sees is the complexity of cyber systems and software, which can have second, third, and fourth order effects.  “Complexity raises the cost of the attack surface, increases the attack surface, raises the number of vulnerabilities and exploits human weaknesses,” says Touhill. “The No. 1 thing we need to be paying attention to is privacy when it comes to AI because AI can unearth and discover knowledge from data we already have. While it gives us greater insights at greater velocities, we need to be careful that we take precautions to better protect our privacy, civil rights and civil liberties.” 

By TREVOR HOGG Images courtesy of Pixar. The character design of Glordon is based on a tardigrade, which is a microscopic water bear. Rather than look at the unknown as something to be feared, Pixar has decided to do some wish fulfillment with Elio, where a lonely adolescent astrophile gets abducted by aliens and is mistaken as the leader of Earth. Originally conceived and directed by Adrian Molina, the coming-of-age science fiction adventure was shepherded by Domee Shi and Madeline Sharafian, who had previously worked together on Turning Red. “Space is often seen as dark, mysterious and scary, but there is also so much hope, wonder and curiosity,” notes Shi, director of Elio. “It’s like anything is ‘out there.’ Elio captures how a lot of us feel at different points of our lives, when we were kids like him, or even now wanting to be off of this current planet because it’s just too much. For Elio, it’s a rescue. I feel that there’s something so universal about that feeling of wanting to be taken away and taken care of. To know that you’re not alone and somebody chose you and picked you up.” The character design of Glordon is based on a tardigrade, which is a microscopic water bear. There is a stark contrast between how Earth and the alien world, known as the Communiverse, are portrayed. “The more we worked with the animators on Glordon and Helix, they began to realize that Domee and I respond positively when those [alien] characters are exaggerated, made cute, round and chubby,” states Sharafian, director of Elio. “That automatically started to differentiate the way the Earth and space feel.” A certain question had to be answered when designing the United Nations-inspired Communiverse. “It was coming from a place of this lonely kid who feels like no one wants him on Earth,” Shi explains. “What would be heaven and paradise for him? The Communiverse was built around that idea.” A sense of belonging is an important theme. “It’s also inspired by Adrian Molina’s backstory, and our backstories too, of going to animation college,” Sharafian remarks. “For the first time, we said, ‘This is where everybody like me is!’” Green is the thematic color for Elio. Visual effects are an important storytelling tool. “Especially, for our movie, which is about this boy going to this crazy incredible world of the Communiverse,” Shi observes. “It has to be dazzling and look spectacular on the big screen and feel like paradise. Elio is such a visual feast, and you do feel like, ‘I want to stay here no matter what. I can’t believe that this place even exists.’ Visual effects are a powerful tool to help you feel what the characters are feeling.” A wishlist became a reality for the directors. “Claudia Chung Sanii [Visual Effects Supervisor] gave Domee and me carte blanche for wish fulfillment for ourselves,” Sharafian remarks. “What do you want Elio’s outfit in space to look like? It was a difficult costume, but now when we watch the movie, we’re all so proud of it. Elio looks fabulous, and he’s so happy to be wearing that outfit. Who would want to take that off?” The Communiverse was meant to feel like a place that a child would love to visit and explore. Methodology rather than technology went through the biggest change for the production. “The Communiverse is super complex and has lots of moving pieces. But there’s not much CG can’t do anymore,” notes Claudia Chung Sanii. “Elemental did effects characters. We did long curly hair, dresses, capes, water and fire. What we hadn’t done before was be a part of that design process. How do we get lighting into layout? How do we see the shaders in animation in layout? The tools department was working on a software called Luna which does that. I went to the tools department and asked, ‘Can I play around with it?’ They were like, ‘Okay. But it’s not ready yet.’ Tools will basically be bringing RenderMan and an interactive lighting workflow to the pipeline across all of these DCCs. Because we light in Katana, you can’t get back upstream. The conceit that we were dipping our toe in on Elio was, ‘Whatever you do in lighting, anyone on the pipeline can see it.’” The influence of microscopic forms and macro photography grounded the Communiverse in natural phenomena. The variety in the Communiverse is a contrast to the regimented world on the military base. There were no departmental borders, in particular with cinematography. “We had our layout and lighting DPs start on the same day. Derek Williams wouldn’t shoot anything without Jordan Rempel, our lighting DP, seeing it,” Sanii states. “Jordan would drop in lighting and start doing key lighting as Derek’s team was laying out. It wasn’t like you had to hit the render button, wait for the render to come up and go, ‘Oh, my god, it’s dark! I didn’t know that it was nighttime.’” A new term was adopted. “Meredith Hom [Production Manager] and I pulled the entire crew and leadership into this mental concept that we called the ‘college project.’ For some of us, college was a time when we didn’t have titles and crafts. You begged, borrowed and stole to hit that deadline. So much of our world has become linear in our process that I wanted to break that down to, ‘No. We’re all working together. The scope of this film is too large for us to wait for each other to finish our piece. If this person is slammed, fine. Figure out a different idea to do it with what tools you have.’” Directors Domee Shi and Madeline Sharafian are drawn to chubby, exaggerated and cute characters. Forgoing the word ‘no’ led to the technology breaking down. “I remember times when crowds [department] is dressing all of the aliens and because of forgetting to constrain it to the Communiverse, they all show up at the origin, and you’re going, ‘Why is there a whole party going on over there?’” Sanii laughs. “On Elio, it was always forward. There were no rules about locking things down or not installing over the weekend. It was always like, ‘Put it all in, and we’ll deal with it on Monday.’ There would be some funny stuff. We never QC’d something before walking it into the room. Everyone saw how the sausage was made. It was fun and not fun for Harley Jessup [Production Designer] because sometimes there would be a big thing in the middle screen, and he would say, ‘Is that finished?’ There was no way we could get through this film if we kept trying to fix the thing that broke.” An aerial image of Elio as he attempts to get abducted by aliens. Part of the design of the Coummuniverse was inspired by Chinese puzzle balls. A former visual effects art director at ILM, Harley Jessup found his previous experiences on projects like Innerspace to be helpful on Elio. “I liked that the directors wanted to build on the effects films from the 1980s and early 1990s,” reflects Jessup. “I was there and part of that. It was fun to look back. At the time, the techniques were all practical, matte paintings and miniatures, which are fun to work with, but without the safety net of CG. One thing Dennis Muren [VES] was keen on, was how people see things like the natural phenomenon you might see in a microscopic or macro photography form. We were using that. I was looking at the mothership of Close Encounters of the Third Kind, which Dennis shot when he was a young artist. It was nice to be able to bring all of that history to this film.” Earth was impacted by a comment made by Pete Docter (CCO, Pixar). “He said, ‘The military base should feel like a parking lot,” Jessup reveals. “You should know why Elio wants to be anywhere else. And the Communiverse needs to be inviting. We built a lot of contrast into those two worlds. The brutalist architecture on the military base, with its hard edges and heavy horizontal forms close to the earth, needed to be harsh but beautiful in its own way, so we tried for that. The Communiverse would be in contrast and be all curves, translucent surfaces and stained-glass backlit effects. Things were wide open about what it could be because each of the aliens are from a different climate and gravity. There are some buildings that are actually upside down on it, and the whole thing is rotating inside like clockwork. It is hopefully an appealing, fun world. It’s not a dystopian outer space.” Exploring various facial expressions for Elio. A tough character to get right was Aunt Olga, who struggles to be the guardian of her nephew. Character designs of Elio and Glordon. which shows them interacting with each other. Architecture was devised to reflect the desired tone for scenes. “In the Grand Assembly Hall where each alien has a desk and booth, the booth is shaped like an eyelid that can close or open,” Jessup explains. “It increases the feeling that they’re evaluating and observing Elio and each of the candidates that have come to join the Communiverse.” A couple of iconic cinematic franchises were avoided for aesthetic reasons. “As much as I love Star Wars and Star Trek, we wanted to be different from those kinds of aliens that are often more humanoid.” Ooooo was the first alien to be designed. “We did Ooooo in collaboration with the effects team, which was small at that time. She was described as a liquid supercomputer. We actually used the wireframe that was turning up and asked, what if it ended up being this network of little lights that are moving around and can express how much she was thinking? Ooooo is Elio’s guide to the Communiverse; her body would deform, so she could become a big screen or reach out and pluck things. Ooooo has an ability like an amoeba to stretch.” Flexibility is important when figuring out shot design. “On Elio, we provided the layout department with a rudimentary version of our environments,” states David Luoh, Sets Supervisor. “It might be simple geometry. We’re not worried necessarily about shading, color and material yet. Things are roughly in place but also built in a way that is flexible. As they’re sorting out the camera and testing out staging, they can move elements of the set around. Maybe this architectural piece needs to be shifted or larger or smaller. There was a variation on what was typically expected of set deliveries of environments to our layout department. That bar was lowered to give the layout department something to work with sooner and also with more flexibility. From their work we get context as to how we partner with our art and design department to build and finalize those environments.” Regional biomes known as disks are part of the Communiverse. “There are aquatic, lush forest, snow and ice, and hot lava disks,” Luoh remarks. “The hot disk is grounded in the desert, volcanic rock and lava, while for the lush disk we looked at interesting plant life found in the world around us.” The Communiverse is a complex geometric form. “We wanted these natural arrangements of alien districts, and that was all happening on this twisting and curving terrain in a way that made traditional dressing approaches clunky. Oftentimes, you’re putting something on the ground or mounted, and the ground is always facing upward. But if you have to dress the wall or ceiling, it becomes a lot more difficult to manipulate and place on something with that dynamic and shape. You have stuff that casts light, is see-through and shifting over time. Ooooo is a living character that looks like electronic circuitry that is constantly moving, and we also have that element in the walls, floors and bubble transport that carry the characters around.” Sets were adjusted throughout the production. “We try to anticipate situations that might come up,” Luoh states. “What if we have a series of shots where you’re getting closer and closer to the Communiverse and you have to bridge the distance between your hero and set extension background? There is a partnership with story, but certainly with our layout camera staging department. As we see shots come out of their work, we know where we need to spend the time to figure out, are we going to see the distant hills in this way? We’re not going to build it until we know because it can be labor-intensive. There is a responsiveness to what we are starting to see as shots get made.” Combining the familiar into something unfamiliar was a process. “There was this curation of being inspired by existing alien sci-fi depictions, but also reaching back into biological phenomena or interesting material because we wanted to ground a lot of those visual elements and ideas in something that people could intuitively grasp on to, even if they were combined or arranged in a way that is surprising, strange and delightful.”

By CHRIS McGOWAN Images courtesy of Warner Bros. Pictures. Final Destination Bloodlines, the sixth installment in the graphic horror series, kicks off with the film’s biggest challenge – deploying an elaborate, large-scale set piece involving the 400-foot-high Skyview Tower restaurant. While there in 1968, young Iris Campbell (Brec Bassinger) has a premonition about the Skyview burning, cracking, crumbling and collapsing. Then, when she sees these events actually starting to happen around her, she intervenes and causes an evacuation of the tower, thus thwarting death’s design and saving many lives. Years later, her granddaughter, Stefani Reyes (Kaitlyn Santa Juana), inherits the vision of the destruction that could have occurred and realizes death is still coming for the survivors. “I knew we couldn’t put the whole [Skyview restaurant] on fire, but Tony [Lazarowich, Special Effects Supervisor] tried and put as much fire as he could safely and then we just built off that [in VFX] and added a lot more. Even when it’s just a little bit of real fire, the lighting and interaction that can’t be simulated, so I think it was a success in terms of blending that practical with the visual.” —Nordin Rahhali, VFX Supervisor The film opens with an elaborate, large-scale set piece involving the 400-foot-high Skyview Tower restaurant – and its collapse. Drone footage was digitized to create a 3D asset for the LED wall so the time of day could be changed as needed. “The set that the directors wanted was very large,” says Nordin Rahhali, VFX Supervisor. “We had limited space options in stages given the scale and the footprint of the actual restaurant that they wanted. It was the first set piece, the first big thing we shot, so we had to get it all ready and going right off the bat. We built a bigger volume for our needs, including an LED wall that we built the assets for.” “We were outside Vancouver at Bridge Studios in Burnaby. The custom-built LED volume was a little over 200 feet in length” states Christian Sebaldt, ASC, the movie’s DP. The volume was 98 feet in diameter and 24 feet tall. Rahhali explains, “Pixomondo was the vendor that we contracted to come in and build the volume. They also built the asset that went on the LED wall, so they were part of our filming team and production shoot. Subsequently, they were also the main vendor doing post, which was by design. By having them design and take care of the asset during production, we were able to leverage their assets, tools and builds for some of the post VFX.” Rahhali adds, “It was really important to make sure we had days with the volume team and with Christian and his camera team ahead of the shoot so we could dial it in.” Built at Bridge Studios in Burnaby outside Vancouver, the custom-built LED volume for events at the Skyview restaurant was over 200 feet long, 98 feet wide and 24 feet tall. Extensive previs with Digital Domain was done to advance key shots. (Photo: Eric Milner) Zach Lipovsky and Adam Stein directed Final Destination Bloodlines for New Line film, distributed by Warner Bros., in which chain reactions of small and big events lead to bloody catastrophes befalling those who have cheated death at some point. Pixomondo was the lead VFX vendor, followed by FOLKS VFX. Picture Shop also contributed. There were around 800 VFX shots. Tony Lazarowich was the Special Effects Supervisor. “The Skyview restaurant involved building a massive set [that] was fire retardant, which meant the construction took longer than normal because they had to build it with certain materials and coat it with certain things because, obviously, it serves for the set piece. As it’s falling into chaos, a lot of that fire was practical. I really jived with what Christian and directors wanted and how Tony likes to work – to augment as much real practical stuff as possible,” Rahhali remarks. “I knew we couldn’t put the whole thing on fire, but Tony tried and put as much fire as he could safely, and then we just built off that [in VFX] and added a lot more. Even when it’s just a little bit of real fire, the lighting and interaction can’t be simulated, so I think it was a success in terms of blending that practical with the visual.” The Skyview restaurant required building a massive set that was fire retardant. Construction on the set took longer because it had to be built and coated with special materials. As the Skyview restaurant falls into chaos, much of the fire was practical. (Photo: Eric Milner) “We got all the Vancouver skyline [with drones] so we could rebuild our version of the city, which was based a little on the Vancouver footprint. So, we used all that to build a digital recreation of a city that was in line with what the directors wanted, which was a coastal city somewhere in the States that doesn’t necessarily have to be Vancouver or Seattle, but it looks a little like the Pacific Northwest.” —Christian Sebaldt, ASC, Director of Photography For drone shots, the team utilized a custom heavy-lift drone with three RED Komodo Digital Cinema cameras “giving us almost 180 degrees with overlap that we would then stitch in post and have a ridiculous amount of resolution off these three cameras,” Sebaldt states. “The other drone we used was a DJI Inspire 3, which was also very good. And we flew these drones up at the height [we needed]. We flew them at different times of day. We flew full 360s, and we also used them for photogrammetry. We got all the Vancouver skyline so we could rebuild our version of the city, which was based a little on the Vancouver footprint. So, we used all that to build a digital recreation of a city that was in line with what the directors wanted, which was a coastal city somewhere in the States that doesn’t necessarily have to be Vancouver or Seattle, but it looks a little like the Pacific Northwest.” Rahhali adds, “All of this allowed us to figure out what we were going to shoot. We had the stage build, and we had the drone footage that we then digitized and created a 3D asset to go on the wall [so] we could change the times of day” Pixomondo built the volume and the asset that went on the LED wall for the Skyview sequence. They were also the main vendor during post. FOLKS VFX and Picture Shop contributed. (Photo: Eric Milner) “We did extensive previs with Digital Domain,” Rahhali explains. “That was important because we knew the key shots that the directors wanted. With a combination of those key shots, we then kind of reverse-engineered [them] while we did techvis off the previs and worked with Christian and the art department so we would have proper flexibility with the set to be able to pull off some of these shots. [For example,] some of these shots required the Skyview restaurant ceiling to be lifted and partially removed for us to get a crane to shoot Paul [Max Lloyd-Jones] as he’s about to fall and the camera’s going through a roof, that we then digitally had to recreate. Had we not done the previs to know those shots in advance, we would not have been able to build that in time to accomplish the look. We had many other shots that were driven off the previs that allowed the art department, construction and camera teams to work out how they would get those shots.” Some shots required the Skyview’s ceiling to be lifted and partially removed to get a crane to shoot Paul Campbell (Max Lloyd-Jones) as he’s about to fall. The character Iris lived in a fortified house, isolating herself methodically to avoid the Grim Reaper. Rahhali comments, “That was a beautiful location [in] GVRD [Greater Vancouver], very cold. It was a long, hard shoot, because it was all nights. It was just this beautiful pocket out in the middle of the mountains. We in visual effects didn’t do a ton other than a couple of clean-ups of the big establishing shots when you see them pull up to the compound. We had to clean up small roads we wanted to make look like one road and make the road look like dirt.” There were flames involved. Sebaldt says, “The explosion [of Iris’s home] was unbelievably big. We had eight cameras on it at night and shot it at high speed, and we’re all going ‘Whoa.’” Rahhali notes, “There was some clean-up, but the explosion was 100% practical. Our Special Effects Supervisor, Tony, went to town on that. He blew up the whole house, and it looked spectacular.” The tattoo shop piercing scene is one of the most talked-about sequences in the movie, where a dangling chain from a ceiling fan attaches itself to the septum nose piercing of Erik Campbell (Richard Harmon) and drags him toward a raging fire. Rahhali observes, “That was very Final Destination and a great Rube Goldberg build-up event. Richard was great. He was tied up on a stunt line for most of it, balancing on top of furniture. All of that was him doing it for real with a stunt line.” Some effects solutions can be surprisingly extremely simple. Rahhali continues, “Our producer [Craig Perry] came up with a great gag [for the] septum ring.” Richard’s nose was connected with just a nose plug that went inside his nostrils. “All that tugging and everything that you’re seeing was real. For weeks and weeks, we were all trying to figure out how to do it without it being a big visual effects thing. ‘How are we gonna pull his nose for real?’ Craig said, ‘I have these things I use to help me open up my nose and you can’t really see them.’ They built it off of that, and it looked great.” Filmmakers spent weeks figuring out how to execute the harrowing tattoo shop scene. A dangling chain from a ceiling fan attaches itself to the septum nose ring of Erik Campbell (Richard Harmon) – with the actor’s nose being tugged by the chain connected to a nose plug that went inside his nostrils. “[S]ome of these shots required the Skyview restaurant ceiling to be lifted and partially removed for us to get a crane to shoot Paul [Campbell] as he’s about to fall and the camera’s going through a roof, that we then digitally had to recreate. Had we not done the previs to know those shots in advance, we would not have been able to build that in time to accomplish the look. We had many other shots that were driven off the previs that allowed the art department, construction and camera teams to work out how they would get those shots.” —Nordin Rahhali, VFX Supervisor Most of the fire in the tattoo parlor was practical. “There are some fire bars and stuff that you’re seeing in there from SFX and the big pool of fire on the wide shots.” Sebaldt adds, “That was a lot of fun to shoot because it’s so insane when he’s dancing and balancing on all this stuff – we were laughing and laughing. We were convinced that this was going to be the best scene in the movie up to that moment.” Rahhali says, “They used the scene wholesale for the trailer. It went viral – people were taking out their septum rings.” Erik survives the parlor blaze only to meet his fate in a hospital when he is pulled by a wheelchair into an out-of-control MRI machine at its highest magnetic level. Rahhali comments, “That is a good combination of a bunch of different departments. Our Stunt Coordinator, Simon Burnett, came up with this hard pull-wire line [for] when Erik flies and hits the MRI. That’s a real stunt with a double, and he hit hard. All the other shots are all CG wheelchairs because the directors wanted to art-direct how the crumpling metal was snapping and bending to show pressure on him as his body starts going into the MRI.” To augment the believability that comes with reality, the directors aimed to capture as much practically as possible, then VFX Supervisor Nordin Rahhali and his team built on that result. (Photo: Eric Milner) A train derailment concludes the film after Stefani and her brother, Charlie, realize they are still on death’s list. A train goes off the tracks, and logs from one of the cars fly though the air and kills them. “That one was special because it’s a hard sequence and was also shot quite late, so we didn’t have a lot of time. We went back to Vancouver and shot the actual street, and we shot our actors performing. They fell onto stunt pads, and the moment they get touched by the logs, it turns into CG as it was the only way to pull that off and the train of course. We had to add all that. The destruction of the houses and everything was done in visual effects.” Erik survives the tattoo parlor blaze only to meet his fate in a hospital when he is crushed by a wheelchair while being pulled into an out-of-control MRI machine. Erik (Richard Harmon) appears about to be run over by a delivery truck at the corner of 21A Ave. and 132A St., but he’s not – at least not then. The truck is actually on the opposite side of the road, and the person being run over is Howard. A rolling penny plays a major part in the catastrophic chain reactions and seems to be a character itself. “The magic penny was a mix from two vendors, Pixomondo and FOLKS; both had penny shots,” Rahhali says. “All the bouncing pennies you see going through the vents and hitting the fan blade are all FOLKS. The bouncing penny at the end as a lady takes it out of her purse, that goes down the ramp and into the rail – that’s FOLKS. The big explosion shots in the Skyview with the penny slowing down after the kid throws it [off the deck] are all Pixomondo shots. It was a mix. We took a little time to find that balance between readability and believability.” Approximately 800 VFX shots were required for Final Destination Bloodlines. (Photo: Eric Milner) Chain reactions of small and big events lead to bloody catastrophes befalling those who have cheated Death at some point in the Final Destination films. From left: Kaitlyn Santa Juana as Stefani Reyes, director Adam Stein, director Zach Lipovsky and Gabrielle Rose as Iris. (Photo: Eric Milner) Rahhali adds, “The film is a great collaboration of departments. Good visual effects are always a good combination of special effects, makeup effects and cinematography; it’s all the planning of all the pieces coming together. For a film of this size, I’m really proud of the work. I think we punched above our weight class, and it looks quite good.”

Get the Popular Science daily newsletter💡 Breakthroughs, discoveries, and DIY tips sent every weekday. Our smartphones have transformed the way we take photos and videos and our relationship to these digital memories. Most of us will snap at least some pictures and clips every day with the gadget that’s always close at hand. If you want to get more creative with photos on your phone, you can. Sometimes you’re going to want to take a picture remotely, without your phone in your hand and your finger over the shutter button—maybe you’re taking a wide shot of a large group, or you want to capture a lot of your surroundings. Not only is this possible, there are multiple ways to go about it, no matter which flavor of phone you own. You can pick the one that you find the easiest to use, or switch between them as you need. Use your smartwatch If you’ve got an Apple Watch, it comes with a Camera app. Image: Raagesh C/Unsplash If you’ve got a smartwatch to match your smartphone, you can use it to take photos remotely, as long as you’re within about 33 feet (10 meters) of the phone. Get your handset in position first, then load up the relevant app on your watch—though you can then go back and readjust the phone if needed. With the Apple Watch and an iPhone, the app you want on your wrist is the Camera Remote app, which comes preinstalled. A viewfinder screen from your iPhone will appear: Use the digital crown to zoom, and the shutter button (in the middle) to take a shot. By default, a three-second timer is used, but you can change this by tapping the button with the three dots (lower right). For those of you with an Android phone and a Wear OS smartwatch, you can use Google’s default Camera app, which you should find preinstalled on your watch. Launch it from your wrist, and the Camera app should open on your connected phone: You can zoom using the slider on the right, and take a photo (with a three-second delay) by tapping the shutter button with a 3 on it. To change this delay, tap the three lines at the top. Use your voice Settings for Voice Control on iOS. Screenshot: Apple No matter what phone you have, it’ll come with support for voice commands—and one of those commands will let you take photos. This will only work where your phone is close enough to hear you, and where you’re happy to talk to it, but it can be useful in certain situations for remote controlling the camera app. On the iPhone, Siri can open the Camera app but won’t actually take a photo. To enable voice controlled capture, open Settings and choose Accessibility > Voice Control, then turn the feature on. The same page has a Commands menu where you can set up your custom voice command for taking photos, which will work from the viewfinder screen. On Android, it’s even easier: Just say “hey Google, take a photo”—you can even add a number of seconds for a timer countdown. Gemini is now the default assistant for this task: To make sure it responds to voice commands, open the app, tap your profile picture (top right), then choose Settings > “Hey Google ” & Voice Match. Use the timer Configuring the timer on a Pixel phone. Screenshot: Google This is a really straightforward one, and you don’t need any extra apps or devices to get it set up. Your phone’s camera app comes with a timer control, so you can position the shot, set the timer, and then get in the frame. There’s a bit of guesswork involved, especially if you’re using your phone’s rear camera (as you won’t be able to see yourself), but it’s a simple option. On the iPhone, you can tap the arrow near the top of the Camera app screen to reveal extra camera options at the bottom. Scroll through the icons until you reach the one that looks like a stopwatch. Tap this, and you can choose between a 3-second, 5-second, and 10-second delay when you press the shutter button. On Pixel phones, tap the gear icon (lower left in portrait mode) to find the timer control: As on the iPhone, the delay options are 3 seconds, 5 seconds, or 10 seconds. If you’re using the Camera app on a Galaxy phone, tap the four dots (to the right in portrait mode), then the timer icon (which looks like a stopwatch), and you get the same delay options. Use another method The latest Pixel phones have a Connected Cameras feature too. Screenshot: Google You’ve got yet more options for this if you need them. One is to use a simple Bluetooth clicker as a remote control: There are a whole host to choose from, such as this CamKix model that will cost you a mere $5.49. They work across iOS and Android and are easy to connect to your camera app. If you have two Pixel 9 phones, you can also use a special feature called Connected Cameras. You can find it from Settings by tapping Connected devices > Connection preferences > Connected Cameras: You get a brief explanation of what the feature does, and you can turn it on via the Use Connected Cameras toggle switch. This is a niche use case, as it only works with two handsets from the Pixel 9 series (at least for now). But if those are the phones you and your family have, you can use one to take photos through the camera of the other; head to the official guide from Google for more details on how it works.