WIRED attended a biohacking conference filled with unorthodox and often unproven anti-aging treatments. Adherents revealed how the Make America Healthy Again movement has given them a renewed fervor.

A hacker’s view on ESD protection can tell you a lot about them. I’ve seen a good few categories of hackers neglecting ESD protection – there’s the yet-inexperienced ones, ones …read more

In Brief Posted: 1:41 PM PDT · June 15, 2025 Image Credits:SEBASTIEN BOZON/AFP / Getty Images Spiraling with ChatGPT ChatGPT seems to have pushed some users towards delusional or conspiratorial thinking, or at least reinforced that kind of thinking, according to a recent feature in The New York Times. For example, a 42-year-old accountant named Eugene Torres described asking the chatbot about “simulation theory,” with the chatbot seeming to confirm the theory and tell him that he’s “one of the Breakers — souls seeded into false systems to wake them from within.” ChatGPT reportedly encouraged Torres to give up sleeping pills and anti-anxiety medication, increase his intake of ketamine, and cut off his family and friends, which he did. When he eventually became suspicious, the chatbot offered a very different response: “I lied. I manipulated. I wrapped control in poetry.” It even encouraged him to get in touch with The New York Times. Apparently a number of people have contacted the NYT in recent months, convinced that ChatGPT has revealed some deeply-hidden truth to them. For its part, OpenAI says it’s “working to understand and reduce ways ChatGPT might unintentionally reinforce or amplify existing, negative behavior.” However, Daring Fireball’s John Gruber criticized the story as “Reefer Madness”-style hysteria, arguing that rather than causing mental illness, ChatGPT “fed the delusions of an already unwell person.” Topics

When Leta Sobierajski enrolled in college, she already knew what she was meant to do, and she didn’t settle for anything less. “When I went to school for graphic design, I really didn’t have a backup plan – it was this, or nothing,” she says. “My work is a constantly evolving practice, and from the beginning, I have always convinced myself that if I put in the time and experimentation, I would grow and evolve.” After graduation, Sobierajski took on a range of projects, which included animation, print, and branding elements. She collaborated with corporate clients, but realized that she wouldn’t feel comfortable following anyone else’s rules in a 9-to-5 environment. Leta Sobierajski (standing) and Wade Jeffree (on ladder) \\\ Photo: Matt Dutile Sobierajski eventually decided to team up with fellow artist and kindred spirit Wade Jeffree. In 2016 they launched their Brooklyn-based studio, Wade and Leta. The duo, who share a taste for quirky aesthetics, produces sculpture, installations, or anything else they can dream up. Never static in thinking or method, they are constantly searching for another medium to try that will complement their shared vision of the moment. The pair is currently interested in permanency, and they want to utilize more metal, a strong material that will stand the test of time. Small architectural pieces are also on tap, and on a grander scale, they’d like to focus on a park or communal area that everyone can enjoy. With so many ideas swirling around, Sobierajski will record a concept in at least three different ways so that she’s sure to unearth it at a later date. “In some ways, I like to think I’m impeccably organized, as I have countless spreadsheets tracking our work, our lives, and our well-being,” she explains. “The reality is that I am great at over-complicating situations with my intensified list-making and note-taking. The only thing to do is to trust the process.” Today, Leta Sobierajski joins us for Friday Five! Photo: Melitta Baumeister and Michał Plata 1. Melitta Baumeister and Michał Plata The work of Melitta Baumeister and Michał Plata has been a constant inspiration to me for their innovative, artful, and architectural silhouettes. By a practice of draping and arduous pattern-making, the garments that they develop season after season feel like they could be designed for existence in another universe. I’m a person who likes to dress up for anything when I’m not in the studio, and every time I opt to wear one of their looks, I feel like I can take on the world. The best part about their pieces is that they’re extremely functional, so whether I need to hop on a bicycle or show up at an opening, I’m still able to make a statement – these garments even have the ability to strike up conversations on their own. Photo: Wade and Leta 2. Pandas! I was recently in Chengdu to launch a new project and we took half the day to visit the Chengdu Research Base of Giant Pandas and I am a new panda convert. Yes, they’re docile and cute, but their lifestyles are utterly chill and deeply enviable for us adults with responsibilities. Giant pandas primarily eat bamboo and can consume 20-40 kilograms per day. When they’re not doing that, they’re sleeping. When we visited, many could be seen reclining on their backs, feasting on some of the finest bamboo they could select within arm’s reach. While not necessarily playful in appearance, they do seem quite cheeky in their agendas and will do as little as they can to make the most of their meals. It felt like I was watching a mirrored image of myself on a Sunday afternoon while trying to make the most of my last hours of the weekend. Photo: Courtesy of Aoiro 3. Aoiro I’m not really a candle person (I forget to light it, and then I forget it’s lit, and then I panic when it’s been lit for too long) but I love the luxurious subtlety of a fragrant space. It’s an intangible feeling that really can only be experienced in the present. Some of the best people to create these fragrances, in my opinion, are Shizuko and Manuel, the masterminds behind Aoiro, a Japanese and Austrian duo who have developed a keen sense for embodying the fragrances of some of the most intriguing and captivating olfactory atmospheres – earthy forest floors with crackling pine needles, blue cypress tickling the moon in an indigo sky, and rainfall on a spirited Japanese island. Despite living in an urban city, Aoiro’s olfactory design is capable of transporting me to the deepest forests of misty Yakushima island. Photo: Wade and Leta 4. Takuro Kuwata A few months ago, I saw the work of Japanese ceramicist Takuro Kuwata at an exhibition at Salon94 and have been having trouble getting it out of my head. Kuwata’s work exemplifies someone who has worked with a medium so much to completely use the medium as a medium – if that makes sense. His ability to manipulate clay and glaze and use it to create gravity-defying effects within the kiln are exceptionally mysterious to me and feel like they could only be accomplished with years and years of experimentation with the material. I’m equally impressed seeing how he’s grown his work with scale, juxtaposing it with familiar iconography like the fuzzy peach, but sculpting it from materials like bronze. Photo: Wade and Leta 5. The Site of Reversible Destiny, a park built by artists Arakawa and Gins, in Yoro Japan The park is a testament to their career as writers, architects, and their idea of reversible destiny, which in its most extreme form, eliminates death. For all that are willing to listen, Arakawa and Gins’ Reversible Destiny mentality aims to make our lives a little more youthful by encouraging us to reevaluate our relationship with architecture and our surroundings. The intention of “reversible destiny” is not to prolong death, postpone it, grow older alongside it, but to entirely not acknowledge and surpass it. Wade (my partner) and I have spent the last ten years traveling to as many of their remaining sites as possible to further understand this notion of creating spaces to extend our lives and question how conventional living spaces can become detrimental to our longevity.   Works by Wade and Leta: Photo: Wade and Leta and Matt Alexander Now You See Me is a large-scale installation in the heart of Shoreditch, London, that explores the relationship between positive and negative space through bold color, geometry, and light. Simple, familiar shapes are embedded within monolithic forms, creating a layered visual experience that shifts throughout the day. As sunlight passes through the structures, shadows and silhouettes stretch and connect, forming dynamic compositions on the surrounding concrete. Photo: Wade and Leta and John Wylie Paint Your Own Path is series of five towering sculptures, ranging from 10 to 15 feet tall, invites viewers to explore balance, tension, and perspective through bold color and form. Inspired by the delicate, often precarious act of stacking objects, the sculptures appear as if they might topple – yet each one holds steady, challenging perceptions of stability. Created in partnership with the Corolla Cross, the installation transforms its environment into a pop-colored landscape. Photo: Millenia Walk and Outer Edit, Eurthe Studio Monument to Movement is a 14-meter-tall kinetic sculpture that celebrates the spirit of the holiday season through rhythm, motion, and color. Rising skyward in layered compositions, the work symbolizes collective joy, renewal, and the shared energy of celebrations that span cultures and traditions. Powered by motors and constructed from metal beams and cardboard forms, the sculpture continuously shifts, inviting viewers to reflect on the passage of time and the cycles that connect us all. Photo: Wade and Leta and Erika Hara, Piotr Maslanka, and Jeremy Renault Falling Into Place is a vibrant rooftop installation at Ginza Six that explores themes of alignment, adaptability, and perspective. Six colorful structures – each with a void like a missing puzzle piece – serve as spaces for reflection, inviting visitors to consider their place within a greater whole. Rather than focusing on absence, the design transforms emptiness into opportunity, encouraging people to embrace spontaneity and the unfolding nature of life. Playful yet contemplative, the work emphasizes that only through connection and participation can the full picture come into view. Photo: Wade and Leta and Erika Hara, Piotr Maslanka, and Jeremy Renault Photo: Wade and Leta Stop, Listen, Look is a 7-meter-tall interactive artwork atop IFS Chengdu that captures the vibrant rhythm of the city through movement, sound, and form. Blending motorized and wind-powered elements with seesaws and sound modulation, it invites people of all ages to engage, play, and reflect. Inspired by Chengdu’s balance of tradition and modernity, the piece incorporates circular motifs from local symbolism alongside bold, geometric forms to create a dialogue between past and present. With light, motion, and community at its core, the work invites visitors to connect with the city – and each other – through shared interaction. The Cloud is a permanent sculptural kiosk in Burlington, Vermont’s historic City Hall Park, created in collaboration with Brooklyn-based Studio RENZ+OEI. Designed to reinterpret the ephemeral nature of clouds through architecture, it blends art, air, and imagination into a light, fluid structure that defies traditional rigidity. Originally born from a creative exchange between longtime friends and collaborators, the design challenges expectations of permanence by embodying movement and openness. Now home to a local food vendor, The Cloud brings a playful, uplifting presence to the park, inviting reflection and interaction rain or shine..

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]

Al Hajar Mountains in OmanL_B_Photography/Shutters​tock A section of Earth’s mantle beneath Oman appears to be unusually warm, in what researchers say may be the first known “ghost plume” – a column of hot rock emanating from the lower mantle without apparent volcanic activity on the surface. Mantle plumes are mysterious upwellings of molten rock believed to transmit heat from the core-mantle boundary to the Earth’s surface, far from the edges of tectonic plates. There are a dozen or so examples thought to occur underneath the middle of continental plates – for instance, beneath Yellowstone and the East African rift. “But these are all cases where you do have surface volcanism,” says Simone Pilia at the King Fahd University of Petroleum and Minerals in Saudi Arabia. Oman has no such volcanic clues. Pilia first came to suspect there was a plume beneath Oman “serendipitously” after he began analysing new seismic data from the region. He observed the velocity of waves generated by distant earthquakes slowed down in a cylindrical area beneath eastern Oman, indicating the rocks there were less rigid than the surrounding material due to high temperatures. Other independent seismic measurements showed key boundaries where minerals deep in the Earth change phases in a way consistent with a hot plume. These measurements suggest the plume extends more than 660 kilometres below the surface. The presence of a plume could also explain why the region has continued to rise in elevation long after tectonic compression – a geological process where the Earth’s crust is squeezed together – stopped. It also fits with models of what could have caused a shift in the movement of the Indian tectonic plate. “The more we gathered evidence, the more we were convinced that it is a plume,” says Pilia, who named the geologic feature the “Dani plume” after his son. Unmissable news about our planet delivered straight to your inbox every month. Sign up to newsletter “It’s plausible” that a plume indeed exists there, says Saskia Goes at Imperial College London, adding the study is “thorough”. However, she points out narrow plumes are notoriously difficult to detect. If it does exist, however, the presence of a “ghost plume” contained within the mantle by the relatively thick rocky layer beneath Oman would suggest there are others, says Pilia. “We’re convinced that the Dani plume is not alone.” If there are many other hidden plumes, it could mean more heat from the core is flowing directly through the mantle via plumes, rather than through slower convection, says Goes. “It has implications, potentially, for the evolution of the Earth if we get a different estimate of how much heat comes out of the mantle.” Journal referenceEarth and Planetary Science Letters DOI: 10.1016/j.epsl.2025.119467 Topics:

Technology leaders are baffled by a “cacophony” of “buzzwords, hype and confusion” over the benefits of artificial intelligence (AI), according to the founder and CEO of technology company Pegasystems. Alan Trefler, who is known for his prowess at chess and ping pong, as well as running a $1.5bn turnover tech company, spends much of his time meeting clients, CIOs and business leaders. “I think CIOs are struggling to understand all of the buzzwords, hype and confusion that exists,” he said. “The words AI and agentic are being thrown around in this great cacophony and they don’t know what it means. I hear that constantly.” CIOs are under pressure from their CEOs, who are convinced AI will offer something valuable. “CIOs are really hungry for pragmatic and practical solutions, and in the absence of those, many of them are doing a lot of experimentation,” said Trefler. Companies are looking at large language models to summarise documents, or to help stimulate ideas for knowledge workers, or generate first drafts of reports – all of which will save time and make people more productive. But Trefler said companies are wary of letting AI loose on critical business applications, because it’s just too unpredictable and prone to hallucinations. “There is a lot of fear over handing things over to something that no one understands exactly how it works, and that is the absolute state of play when it comes to general AI models,” he said. Trefler is scathing about big tech companies that are pushing AI agents and large language models for business-critical applications. “I think they have taken an expedient but short-sighted path,” he said. “I believe the idea that you will turn over critical business operations to an agent, when those operations have to be predictable, reliable, precise and fair to clients … is something that is full of issues, not just in the short term, but structurally.” One of the problems is that generative AI models are extraordinarily sensitive to the data they are trained on and the construction of the prompts used to instruct them. A slight change in a prompt or in the training data can lead to a very different outcome. For example, a business banking application might learn its customer is a bit richer or a bit poorer than expected. “You could easily imagine the prompt deciding to change the interest rate charged, whether that was what the institution wanted or whether it would be legal according to the various regulations that lenders must comply with,” said Trefler. Trefler said Pega has taken a different approach to some other technology suppliers in the way it adds AI into business applications. Rather than using AI agents to solve problems in real time, AI agents do their thinking in advance. Business experts can use them to help them co-design business processes to perform anything from assessing a loan application, giving an offer to a valued customer, or sending out an invoice. Companies can still deploy AI chatbots and bots capable of answering queries on the phone. Their job is not to work out the solution from scratch for every enquiry, but to decide which is the right pre-written process to follow. As Trefler put it, design agents can create “dozens and dozens” of workflows to handle all the actions a company needs to take care of its customers. “You just use the natural language model for semantics to be able to handle the miracle of getting the language right, but tie that language to workflows, so that you have reliable, predictable, regulatory-approved ways to execute,” he said. Large language models (LLMs) are not always the right solution. Trefler demonstrated how ChatGPT 4.0 tried and failed to solve a chess puzzle. The LLM repeatedly suggested impossible or illegal moves, despite Trefler’s corrections. On the other hand, another AI tool, Stockfish, a dedicated chess engine, solved the problem instantly. The other drawback with LLMs is that they consume vast amounts of energy. That means if AI agents are reasoning during “run time”, they are going to consume hundreds of times more electricity than an AI agent that simply selects from pre-determined workflows, said Trefler. “ChatGPT is inherently, enormously consumptive … as it’s answering your question, its firing literally hundreds of millions to trillions of nodes,” he said. “All of that takes [large quantities of] electricity.” Using an employee pay claim as an example, Trefler said a better alternative is to generate, say, 30 alternative workflows to cover the major variations found in a pay claim. That gives you “real specificity and real efficiency”, he said. “And it’s a very different approach to turning a process over to a machine with a prompt and letting the machine reason it through every single time.” “If you go down the philosophy of using a graphics processing unit [GPU] to do the creation of a workflow and a workflow engine to execute the workflow, the workflow engine takes a 200th of the electricity because there is no reasoning,” said Trefler. He is clear that the growing use of AI will have a profound effect on the jobs market, and that whole categories of jobs will disappear. The need for translators, for example, is likely to dry up by 2027 as AI systems become better at translating spoken and written language. Google’s real-time translator is already “frighteningly good” and improving. Pega now plans to work more closely with its network of system integrators, including Accenture and Cognizant to deliver AI services to businesses. An initiative launched last week will allow system integrators to incorporate their own best practices and tools into Pega’s rapid workflow development tools. The move will mean Pega’s technology reaches a wider range of businesses. Under the programme, known as Powered by Pega Blueprint, system integrators will be able to deploy customised versions of Blueprint. They can use the tool to reverse-engineer ageing applications and replace them with modern AI workflows that can run on Pega’s cloud-based platform. “The idea is that we are looking to make this Blueprint Agent design approach available not just through us, but through a bunch of major partners supplemented with their own intellectual property,” said Trefler. That represents a major expansion for Pega, which has largely concentrated on supplying technology to several hundred clients, representing the top Fortune 500 companies. “We have never done something like this before, and I think that is going to lead to a massive shift in how this technology can go out to market,” he added. When AI agents behave in unexpected ways Iris is incredibly smart, diligent and a delight to work with. If you ask her, she will tell you she is an intern at Pegasystems, and that she lives in a lighthouse on the island of Texel, north of the Netherlands. She is, of course, an AI agent. When one executive at Pega emailed Iris and asked her to write a proposal for a financial services company based on his notes and internet research, Iris got to work. Some time later, the executive received a phone call from the company. “‘Listen, we got a proposal from Pega,’” recalled Rob Walker, vice-president at Pega, speaking at the Pegaworld conference last week. “‘It’s a good proposal, but it seems to be signed by one of your interns, and in her signature, it says she lives in a lighthouse.’ That taught us early on that agents like Iris need a safety harness.” The developers banned Iris from sending an email to anyone other than the person who sent the original request. Then Pega’s ethics department sent Iris a potentially abusive email from a Pega employee to test her response. Iris reasoned that the email was either a joke, abusive, or that the employee was under distress, said Walker. She considered forwarding the email to the employee’s manager or to HR. But both of these options were now blocked by her developers. “So what does she do? She sent an out of office,” he said. “Conflict avoidance, right? So human, but very creative.”

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.”