• Amazing news in the world of mathematics! A brilliant student has cracked a long-standing problem about the limits of addition, revealing hidden patterns that emerge when addition seems impossible! This breakthrough not only illuminates the complexities of math but also reminds us that every challenge holds a key to new discoveries.

    Let this inspire you to embrace challenges, knowing that every obstacle can lead to incredible insights! Keep pushing the boundaries, and remember: with determination and creativity, anything is possible!

    #Mathematics #Inspiration #Breakthrough #StudentSuccess #PositiveVibes
    🎉✨ Amazing news in the world of mathematics! A brilliant student has cracked a long-standing problem about the limits of addition, revealing hidden patterns that emerge when addition seems impossible! 🌟🧠 This breakthrough not only illuminates the complexities of math but also reminds us that every challenge holds a key to new discoveries. 💡💖 Let this inspire you to embrace challenges, knowing that every obstacle can lead to incredible insights! Keep pushing the boundaries, and remember: with determination and creativity, anything is possible! 🚀💪 #Mathematics #Inspiration #Breakthrough #StudentSuccess #PositiveVibes
    Student Solves a Long-Standing Problem About the Limits of Addition
    A new proof illuminates the hidden patterns that emerge when addition becomes impossible.
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  • Ansys: R&D Engineer II (Remote - East Coast, US)

    Requisition #: 16890 Our Mission: Powering Innovation That Drives Human Advancement When visionary companies need to know how their world-changing ideas will perform, they close the gap between design and reality with Ansys simulation. For more than 50 years, Ansys software has enabled innovators across industries to push boundaries by using the predictive power of simulation. From sustainable transportation to advanced semiconductors, from satellite systems to life-saving medical devices, the next great leaps in human advancement will be powered by Ansys. Innovate With Ansys, Power Your Career. Summary / Role Purpose The R&D Engineer II contributes to the development of software products and supporting systems. In this role, the R&D Engineer II will collaborate with a team of expert professionals to understand customer requirements and accomplish development objectives. Key Duties and Responsibilities Performs moderately complex development activities, including the design, implementation, maintenance, testing and documentation of software modules and sub-systems Understands and employs best practices Performs moderately complex bug verification, release testing and beta support for assigned products. Researches problems discovered by QA or product support and develops solutions Understands the marketing requirements for a product, including target environment, performance criteria and competitive issues Works under the general supervision of a development manager Minimum Education/Certification Requirements and Experience BS in Computer Science, Applied Mathematics, Engineering, or other natural science disciplines with 3-5 years' experience or MS with minimum 2 years experience Working experience within technical software development proven by academic, research, or industry projects. Good understanding and skills in object-oriented programming Experience with Java and C# / .NET Role can be remote, must be based on the East Coast due to timezone Preferred Qualifications and Skills Experience with C++, Python, in addition to Java and C# / .NET Knowledge of Task-Based Asynchronous design patternExposure to model-based systems engineering concepts Working knowledge of SysML Know-how on cloud computing technologies like micro-service architectures, RPC frameworks, REST APIs, etc. Knowledge of software security best practices Experience working on an Agile software development team Technical knowledge and experience with various engineering tools and methodologies, such as Finite Element simulation, CAD modeling, and Systems Architecture modelling is a plus Ability to assist more junior developers on an as-needed basis Ability to learn quickly and to collaborate with others in a geographically distributed team Excellent communication and interpersonal skills At Ansys, we know that changing the world takes vision, skill, and each other. We fuel new ideas, build relationships, and help each other realize our greatest potential. We are ONE Ansys. We operate on three key components: our commitments to stakeholders, our values that guide how we work together, and our actions to deliver results. As ONE Ansys, we are powering innovation that drives human advancement Our Commitments:Amaze with innovative products and solutionsMake our customers incredibly successfulAct with integrityEnsure employees thrive and shareholders prosper Our Values:Adaptability: Be open, welcome what's nextCourage: Be courageous, move forward passionatelyGenerosity: Be generous, share, listen, serveAuthenticity: Be you, make us stronger Our Actions:We commit to audacious goalsWe work seamlessly as a teamWe demonstrate masteryWe deliver outstanding resultsVALUES IN ACTION Ansys is committed to powering the people who power human advancement. We believe in creating and nurturing a workplace that supports and welcomes people of all backgrounds; encouraging them to bring their talents and experience to a workplace where they are valued and can thrive. Our culture is grounded in our four core values of adaptability, courage, generosity, and authenticity. Through our behaviors and actions, these values foster higher team performance and greater innovation for our customers. We're proud to offer programs, available to all employees, to further impact innovation and business outcomes, such as employee networks and learning communities that inform solutions for our globally minded customer base. WELCOME WHAT'S NEXT IN YOUR CAREER AT ANSYS At Ansys, you will find yourself among the sharpest minds and most visionary leaders across the globe. Collectively, we strive to change the world with innovative technology and transformational solutions. With a prestigious reputation in working with well-known, world-class companies, standards at Ansys are high - met by those willing to rise to the occasion and meet those challenges head on. Our team is passionate about pushing the limits of world-class simulation technology, empowering our customers to turn their design concepts into successful, innovative products faster and at a lower cost. Ready to feel inspired? Check out some of our recent customer stories, here and here . At Ansys, it's about the learning, the discovery, and the collaboration. It's about the "what's next" as much as the "mission accomplished." And it's about the melding of disciplined intellect with strategic direction and results that have, can, and do impact real people in real ways. All this is forged within a working environment built on respect, autonomy, and ethics.CREATING A PLACE WE'RE PROUD TO BEAnsys is an S&P 500 company and a member of the NASDAQ-100. We are proud to have been recognized for the following more recent awards, although our list goes on: Newsweek's Most Loved Workplace globally and in the U.S., Gold Stevie Award Winner, America's Most Responsible Companies, Fast Company World Changing Ideas, Great Place to Work Certified.For more information, please visit us at Ansys is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other protected characteristics.Ansys does not accept unsolicited referrals for vacancies, and any unsolicited referral will become the property of Ansys. Upon hire, no fee will be owed to the agency, person, or entity.Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Full-Stack Programming JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot
    #ansys #rampampd #engineer #remote #east
    Ansys: R&D Engineer II (Remote - East Coast, US)
    Requisition #: 16890 Our Mission: Powering Innovation That Drives Human Advancement When visionary companies need to know how their world-changing ideas will perform, they close the gap between design and reality with Ansys simulation. For more than 50 years, Ansys software has enabled innovators across industries to push boundaries by using the predictive power of simulation. From sustainable transportation to advanced semiconductors, from satellite systems to life-saving medical devices, the next great leaps in human advancement will be powered by Ansys. Innovate With Ansys, Power Your Career. Summary / Role Purpose The R&D Engineer II contributes to the development of software products and supporting systems. In this role, the R&D Engineer II will collaborate with a team of expert professionals to understand customer requirements and accomplish development objectives. Key Duties and Responsibilities Performs moderately complex development activities, including the design, implementation, maintenance, testing and documentation of software modules and sub-systems Understands and employs best practices Performs moderately complex bug verification, release testing and beta support for assigned products. Researches problems discovered by QA or product support and develops solutions Understands the marketing requirements for a product, including target environment, performance criteria and competitive issues Works under the general supervision of a development manager Minimum Education/Certification Requirements and Experience BS in Computer Science, Applied Mathematics, Engineering, or other natural science disciplines with 3-5 years' experience or MS with minimum 2 years experience Working experience within technical software development proven by academic, research, or industry projects. Good understanding and skills in object-oriented programming Experience with Java and C# / .NET Role can be remote, must be based on the East Coast due to timezone Preferred Qualifications and Skills Experience with C++, Python, in addition to Java and C# / .NET Knowledge of Task-Based Asynchronous design patternExposure to model-based systems engineering concepts Working knowledge of SysML Know-how on cloud computing technologies like micro-service architectures, RPC frameworks, REST APIs, etc. Knowledge of software security best practices Experience working on an Agile software development team Technical knowledge and experience with various engineering tools and methodologies, such as Finite Element simulation, CAD modeling, and Systems Architecture modelling is a plus Ability to assist more junior developers on an as-needed basis Ability to learn quickly and to collaborate with others in a geographically distributed team Excellent communication and interpersonal skills At Ansys, we know that changing the world takes vision, skill, and each other. We fuel new ideas, build relationships, and help each other realize our greatest potential. We are ONE Ansys. We operate on three key components: our commitments to stakeholders, our values that guide how we work together, and our actions to deliver results. As ONE Ansys, we are powering innovation that drives human advancement Our Commitments:Amaze with innovative products and solutionsMake our customers incredibly successfulAct with integrityEnsure employees thrive and shareholders prosper Our Values:Adaptability: Be open, welcome what's nextCourage: Be courageous, move forward passionatelyGenerosity: Be generous, share, listen, serveAuthenticity: Be you, make us stronger Our Actions:We commit to audacious goalsWe work seamlessly as a teamWe demonstrate masteryWe deliver outstanding resultsVALUES IN ACTION Ansys is committed to powering the people who power human advancement. We believe in creating and nurturing a workplace that supports and welcomes people of all backgrounds; encouraging them to bring their talents and experience to a workplace where they are valued and can thrive. Our culture is grounded in our four core values of adaptability, courage, generosity, and authenticity. Through our behaviors and actions, these values foster higher team performance and greater innovation for our customers. We're proud to offer programs, available to all employees, to further impact innovation and business outcomes, such as employee networks and learning communities that inform solutions for our globally minded customer base. WELCOME WHAT'S NEXT IN YOUR CAREER AT ANSYS At Ansys, you will find yourself among the sharpest minds and most visionary leaders across the globe. Collectively, we strive to change the world with innovative technology and transformational solutions. With a prestigious reputation in working with well-known, world-class companies, standards at Ansys are high - met by those willing to rise to the occasion and meet those challenges head on. Our team is passionate about pushing the limits of world-class simulation technology, empowering our customers to turn their design concepts into successful, innovative products faster and at a lower cost. Ready to feel inspired? Check out some of our recent customer stories, here and here . At Ansys, it's about the learning, the discovery, and the collaboration. It's about the "what's next" as much as the "mission accomplished." And it's about the melding of disciplined intellect with strategic direction and results that have, can, and do impact real people in real ways. All this is forged within a working environment built on respect, autonomy, and ethics.CREATING A PLACE WE'RE PROUD TO BEAnsys is an S&P 500 company and a member of the NASDAQ-100. We are proud to have been recognized for the following more recent awards, although our list goes on: Newsweek's Most Loved Workplace globally and in the U.S., Gold Stevie Award Winner, America's Most Responsible Companies, Fast Company World Changing Ideas, Great Place to Work Certified.For more information, please visit us at Ansys is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other protected characteristics.Ansys does not accept unsolicited referrals for vacancies, and any unsolicited referral will become the property of Ansys. Upon hire, no fee will be owed to the agency, person, or entity.Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Full-Stack Programming JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot #ansys #rampampd #engineer #remote #east
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    Ansys: R&D Engineer II (Remote - East Coast, US)
    Requisition #: 16890 Our Mission: Powering Innovation That Drives Human Advancement When visionary companies need to know how their world-changing ideas will perform, they close the gap between design and reality with Ansys simulation. For more than 50 years, Ansys software has enabled innovators across industries to push boundaries by using the predictive power of simulation. From sustainable transportation to advanced semiconductors, from satellite systems to life-saving medical devices, the next great leaps in human advancement will be powered by Ansys. Innovate With Ansys, Power Your Career. Summary / Role Purpose The R&D Engineer II contributes to the development of software products and supporting systems. In this role, the R&D Engineer II will collaborate with a team of expert professionals to understand customer requirements and accomplish development objectives. Key Duties and Responsibilities Performs moderately complex development activities, including the design, implementation, maintenance, testing and documentation of software modules and sub-systems Understands and employs best practices Performs moderately complex bug verification, release testing and beta support for assigned products. Researches problems discovered by QA or product support and develops solutions Understands the marketing requirements for a product, including target environment, performance criteria and competitive issues Works under the general supervision of a development manager Minimum Education/Certification Requirements and Experience BS in Computer Science, Applied Mathematics, Engineering, or other natural science disciplines with 3-5 years' experience or MS with minimum 2 years experience Working experience within technical software development proven by academic, research, or industry projects. Good understanding and skills in object-oriented programming Experience with Java and C# / .NET Role can be remote, must be based on the East Coast due to timezone Preferred Qualifications and Skills Experience with C++, Python, in addition to Java and C# / .NET Knowledge of Task-Based Asynchronous design pattern (TAP) Exposure to model-based systems engineering concepts Working knowledge of SysML Know-how on cloud computing technologies like micro-service architectures, RPC frameworks (e.g., gRPC), REST APIs, etc. Knowledge of software security best practices Experience working on an Agile software development team Technical knowledge and experience with various engineering tools and methodologies, such as Finite Element simulation, CAD modeling, and Systems Architecture modelling is a plus Ability to assist more junior developers on an as-needed basis Ability to learn quickly and to collaborate with others in a geographically distributed team Excellent communication and interpersonal skills At Ansys, we know that changing the world takes vision, skill, and each other. We fuel new ideas, build relationships, and help each other realize our greatest potential. We are ONE Ansys. We operate on three key components: our commitments to stakeholders, our values that guide how we work together, and our actions to deliver results. As ONE Ansys, we are powering innovation that drives human advancement Our Commitments:Amaze with innovative products and solutionsMake our customers incredibly successfulAct with integrityEnsure employees thrive and shareholders prosper Our Values:Adaptability: Be open, welcome what's nextCourage: Be courageous, move forward passionatelyGenerosity: Be generous, share, listen, serveAuthenticity: Be you, make us stronger Our Actions:We commit to audacious goalsWe work seamlessly as a teamWe demonstrate masteryWe deliver outstanding resultsVALUES IN ACTION Ansys is committed to powering the people who power human advancement. We believe in creating and nurturing a workplace that supports and welcomes people of all backgrounds; encouraging them to bring their talents and experience to a workplace where they are valued and can thrive. Our culture is grounded in our four core values of adaptability, courage, generosity, and authenticity. Through our behaviors and actions, these values foster higher team performance and greater innovation for our customers. We're proud to offer programs, available to all employees, to further impact innovation and business outcomes, such as employee networks and learning communities that inform solutions for our globally minded customer base. WELCOME WHAT'S NEXT IN YOUR CAREER AT ANSYS At Ansys, you will find yourself among the sharpest minds and most visionary leaders across the globe. Collectively, we strive to change the world with innovative technology and transformational solutions. With a prestigious reputation in working with well-known, world-class companies, standards at Ansys are high - met by those willing to rise to the occasion and meet those challenges head on. Our team is passionate about pushing the limits of world-class simulation technology, empowering our customers to turn their design concepts into successful, innovative products faster and at a lower cost. Ready to feel inspired? Check out some of our recent customer stories, here and here . At Ansys, it's about the learning, the discovery, and the collaboration. It's about the "what's next" as much as the "mission accomplished." And it's about the melding of disciplined intellect with strategic direction and results that have, can, and do impact real people in real ways. All this is forged within a working environment built on respect, autonomy, and ethics.CREATING A PLACE WE'RE PROUD TO BEAnsys is an S&P 500 company and a member of the NASDAQ-100. We are proud to have been recognized for the following more recent awards, although our list goes on: Newsweek's Most Loved Workplace globally and in the U.S., Gold Stevie Award Winner, America's Most Responsible Companies, Fast Company World Changing Ideas, Great Place to Work Certified (China, Greece, France, India, Japan, Korea, Spain, Sweden, Taiwan, and U.K.).For more information, please visit us at Ansys is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, and other protected characteristics.Ansys does not accept unsolicited referrals for vacancies, and any unsolicited referral will become the property of Ansys. Upon hire, no fee will be owed to the agency, person, or entity.Apply NowLet's start your dream job Apply now Meet JobCopilot: Your Personal AI Job HunterAutomatically Apply to Remote Full-Stack Programming JobsJust set your preferences and Job Copilot will do the rest-finding, filtering, and applying while you focus on what matters. Activate JobCopilot
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  • How AI is reshaping the future of healthcare and medical research

    Transcript       
    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?”          
    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.  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.     
    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 weaknessthat, 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. 
    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 MSRto 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.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.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: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 aboutor 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.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. 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, 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.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 thatversion 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, 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 AGIthat 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.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 seeproduced 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. 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 reelectedjust 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.  
    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.  
    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.  
    #how #reshaping #future #healthcare #medical
    How AI is reshaping the future of healthcare and medical research
    Transcript        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?”           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.  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.      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 weaknessthat, 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.  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 MSRto 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.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.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: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 aboutor 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.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. 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, 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.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 thatversion 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, 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 AGIthat 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.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 seeproduced 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. 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 reelectedjust 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.   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.   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.   #how #reshaping #future #healthcare #medical
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    How AI is reshaping the future of healthcare and medical research
    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]
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  • IBM Plans Large-Scale Fault-Tolerant Quantum Computer by 2029

    IBM Plans Large-Scale Fault-Tolerant Quantum Computer by 2029

    By John P. Mello Jr.
    June 11, 2025 5:00 AM PT

    IBM unveiled its plan to build IBM Quantum Starling, shown in this rendering. Starling is expected to be the first large-scale, fault-tolerant quantum system.ADVERTISEMENT
    Enterprise IT Lead Generation Services
    Fuel Your Pipeline. Close More Deals. Our full-service marketing programs deliver sales-ready leads. 100% Satisfaction Guarantee! Learn more.

    IBM revealed Tuesday its roadmap for bringing a large-scale, fault-tolerant quantum computer, IBM Quantum Starling, online by 2029, which is significantly earlier than many technologists thought possible.
    The company predicts that when its new Starling computer is up and running, it will be capable of performing 20,000 times more operations than today’s quantum computers — a computational state so vast it would require the memory of more than a quindecillionof the world’s most powerful supercomputers to represent.
    “IBM is charting the next frontier in quantum computing,” Big Blue CEO Arvind Krishna said in a statement. “Our expertise across mathematics, physics, and engineering is paving the way for a large-scale, fault-tolerant quantum computer — one that will solve real-world challenges and unlock immense possibilities for business.”
    IBM’s plan to deliver a fault-tolerant quantum system by 2029 is ambitious but not implausible, especially given the rapid pace of its quantum roadmap and past milestones, observed Ensar Seker, CISO at SOCRadar, a threat intelligence company in Newark, Del.
    “They’ve consistently met or exceeded their qubit scaling goals, and their emphasis on modularity and error correction indicates they’re tackling the right challenges,” he told TechNewsWorld. “However, moving from thousands to millions of physical qubits with sufficient fidelity remains a steep climb.”
    A qubit is the fundamental unit of information in quantum computing, capable of representing a zero, a one, or both simultaneously due to quantum superposition. In practice, fault-tolerant quantum computers use clusters of physical qubits working together to form a logical qubit — a more stable unit designed to store quantum information and correct errors in real time.
    Realistic Roadmap
    Luke Yang, an equity analyst with Morningstar Research Services in Chicago, believes IBM’s roadmap is realistic. “The exact scale and error correction performance might still change between now and 2029, but overall, the goal is reasonable,” he told TechNewsWorld.
    “Given its reliability and professionalism, IBM’s bold claim should be taken seriously,” said Enrique Solano, co-CEO and co-founder of Kipu Quantum, a quantum algorithm company with offices in Berlin and Karlsruhe, Germany.
    “Of course, it may also fail, especially when considering the unpredictability of hardware complexities involved,” he told TechNewsWorld, “but companies like IBM exist for such challenges, and we should all be positively impressed by its current achievements and promised technological roadmap.”
    Tim Hollebeek, vice president of industry standards at DigiCert, a global digital security company, added: “IBM is a leader in this area, and not normally a company that hypes their news. This is a fast-moving industry, and success is certainly possible.”
    “IBM is attempting to do something that no one has ever done before and will almost certainly run into challenges,” he told TechNewsWorld, “but at this point, it is largely an engineering scaling exercise, not a research project.”
    “IBM has demonstrated consistent progress, has committed billion over five years to quantum computing, and the timeline is within the realm of technical feasibility,” noted John Young, COO of Quantum eMotion, a developer of quantum random number generator technology, in Saint-Laurent, Quebec, Canada.
    “That said,” he told TechNewsWorld, “fault-tolerant in a practical, industrial sense is a very high bar.”
    Solving the Quantum Error Correction Puzzle
    To make a quantum computer fault-tolerant, errors need to be corrected so large workloads can be run without faults. In a quantum computer, errors are reduced by clustering physical qubits to form logical qubits, which have lower error rates than the underlying physical qubits.
    “Error correction is a challenge,” Young said. “Logical qubits require thousands of physical qubits to function reliably. That’s a massive scaling issue.”
    IBM explained in its announcement that creating increasing numbers of logical qubits capable of executing quantum circuits with as few physical qubits as possible is critical to quantum computing at scale. Until today, a clear path to building such a fault-tolerant system without unrealistic engineering overhead has not been published.

    Alternative and previous gold-standard, error-correcting codes present fundamental engineering challenges, IBM continued. To scale, they would require an unfeasible number of physical qubits to create enough logical qubits to perform complex operations — necessitating impractical amounts of infrastructure and control electronics. This renders them unlikely to be implemented beyond small-scale experiments and devices.
    In two research papers released with its roadmap, IBM detailed how it will overcome the challenges of building the large-scale, fault-tolerant architecture needed for a quantum computer.
    One paper outlines the use of quantum low-density parity checkcodes to reduce physical qubit overhead. The other describes methods for decoding errors in real time using conventional computing.
    According to IBM, a practical fault-tolerant quantum architecture must:

    Suppress enough errors for useful algorithms to succeed
    Prepare and measure logical qubits during computation
    Apply universal instructions to logical qubits
    Decode measurements from logical qubits in real time and guide subsequent operations
    Scale modularly across hundreds or thousands of logical qubits
    Be efficient enough to run meaningful algorithms using realistic energy and infrastructure resources

    Aside from the technological challenges that quantum computer makers are facing, there may also be some market challenges. “Locating suitable use cases for quantum computers could be the biggest challenge,” Morningstar’s Yang maintained.
    “Only certain computing workloads, such as random circuit sampling, can fully unleash the computing power of quantum computers and show their advantage over the traditional supercomputers we have now,” he said. “However, workloads like RCS are not very commercially useful, and we believe commercial relevance is one of the key factors that determine the total market size for quantum computers.”
    Q-Day Approaching Faster Than Expected
    For years now, organizations have been told they need to prepare for “Q-Day” — the day a quantum computer will be able to crack all the encryption they use to keep their data secure. This IBM announcement suggests the window for action to protect data may be closing faster than many anticipated.
    “This absolutely adds urgency and credibility to the security expert guidance on post-quantum encryption being factored into their planning now,” said Dave Krauthamer, field CTO of QuSecure, maker of quantum-safe security solutions, in San Mateo, Calif.
    “IBM’s move to create a large-scale fault-tolerant quantum computer by 2029 is indicative of the timeline collapsing,” he told TechNewsWorld. “A fault-tolerant quantum computer of this magnitude could be well on the path to crack asymmetric ciphers sooner than anyone thinks.”

    “Security leaders need to take everything connected to post-quantum encryption as a serious measure and work it into their security plans now — not later,” he said.
    Roger Grimes, a defense evangelist with KnowBe4, a security awareness training provider in Clearwater, Fla., pointed out that IBM is just the latest in a surge of quantum companies announcing quickly forthcoming computational breakthroughs within a few years.
    “It leads to the question of whether the U.S. government’s original PQCpreparation date of 2030 is still a safe date,” he told TechNewsWorld.
    “It’s starting to feel a lot more risky for any company to wait until 2030 to be prepared against quantum attacks. It also flies in the face of the latest cybersecurity EOthat relaxed PQC preparation rules as compared to Biden’s last EO PQC standard order, which told U.S. agencies to transition to PQC ASAP.”
    “Most US companies are doing zero to prepare for Q-Day attacks,” he declared. “The latest executive order seems to tell U.S. agencies — and indirectly, all U.S. businesses — that they have more time to prepare. It’s going to cause even more agencies and businesses to be less prepared during a time when it seems multiple quantum computing companies are making significant progress.”
    “It definitely feels that something is going to give soon,” he said, “and if I were a betting man, and I am, I would bet that most U.S. companies are going to be unprepared for Q-Day on the day Q-Day becomes a reality.”

    John P. Mello Jr. has been an ECT News Network reporter since 2003. His areas of focus include cybersecurity, IT issues, privacy, e-commerce, social media, artificial intelligence, big data and consumer electronics. He has written and edited for numerous publications, including the Boston Business Journal, the Boston Phoenix, Megapixel.Net and Government Security News. Email John.

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    More in Emerging Tech
    #ibm #plans #largescale #faulttolerant #quantum
    IBM Plans Large-Scale Fault-Tolerant Quantum Computer by 2029
    IBM Plans Large-Scale Fault-Tolerant Quantum Computer by 2029 By John P. Mello Jr. June 11, 2025 5:00 AM PT IBM unveiled its plan to build IBM Quantum Starling, shown in this rendering. Starling is expected to be the first large-scale, fault-tolerant quantum system.ADVERTISEMENT Enterprise IT Lead Generation Services Fuel Your Pipeline. Close More Deals. Our full-service marketing programs deliver sales-ready leads. 100% Satisfaction Guarantee! Learn more. IBM revealed Tuesday its roadmap for bringing a large-scale, fault-tolerant quantum computer, IBM Quantum Starling, online by 2029, which is significantly earlier than many technologists thought possible. The company predicts that when its new Starling computer is up and running, it will be capable of performing 20,000 times more operations than today’s quantum computers — a computational state so vast it would require the memory of more than a quindecillionof the world’s most powerful supercomputers to represent. “IBM is charting the next frontier in quantum computing,” Big Blue CEO Arvind Krishna said in a statement. “Our expertise across mathematics, physics, and engineering is paving the way for a large-scale, fault-tolerant quantum computer — one that will solve real-world challenges and unlock immense possibilities for business.” IBM’s plan to deliver a fault-tolerant quantum system by 2029 is ambitious but not implausible, especially given the rapid pace of its quantum roadmap and past milestones, observed Ensar Seker, CISO at SOCRadar, a threat intelligence company in Newark, Del. “They’ve consistently met or exceeded their qubit scaling goals, and their emphasis on modularity and error correction indicates they’re tackling the right challenges,” he told TechNewsWorld. “However, moving from thousands to millions of physical qubits with sufficient fidelity remains a steep climb.” A qubit is the fundamental unit of information in quantum computing, capable of representing a zero, a one, or both simultaneously due to quantum superposition. In practice, fault-tolerant quantum computers use clusters of physical qubits working together to form a logical qubit — a more stable unit designed to store quantum information and correct errors in real time. Realistic Roadmap Luke Yang, an equity analyst with Morningstar Research Services in Chicago, believes IBM’s roadmap is realistic. “The exact scale and error correction performance might still change between now and 2029, but overall, the goal is reasonable,” he told TechNewsWorld. “Given its reliability and professionalism, IBM’s bold claim should be taken seriously,” said Enrique Solano, co-CEO and co-founder of Kipu Quantum, a quantum algorithm company with offices in Berlin and Karlsruhe, Germany. “Of course, it may also fail, especially when considering the unpredictability of hardware complexities involved,” he told TechNewsWorld, “but companies like IBM exist for such challenges, and we should all be positively impressed by its current achievements and promised technological roadmap.” Tim Hollebeek, vice president of industry standards at DigiCert, a global digital security company, added: “IBM is a leader in this area, and not normally a company that hypes their news. This is a fast-moving industry, and success is certainly possible.” “IBM is attempting to do something that no one has ever done before and will almost certainly run into challenges,” he told TechNewsWorld, “but at this point, it is largely an engineering scaling exercise, not a research project.” “IBM has demonstrated consistent progress, has committed billion over five years to quantum computing, and the timeline is within the realm of technical feasibility,” noted John Young, COO of Quantum eMotion, a developer of quantum random number generator technology, in Saint-Laurent, Quebec, Canada. “That said,” he told TechNewsWorld, “fault-tolerant in a practical, industrial sense is a very high bar.” Solving the Quantum Error Correction Puzzle To make a quantum computer fault-tolerant, errors need to be corrected so large workloads can be run without faults. In a quantum computer, errors are reduced by clustering physical qubits to form logical qubits, which have lower error rates than the underlying physical qubits. “Error correction is a challenge,” Young said. “Logical qubits require thousands of physical qubits to function reliably. That’s a massive scaling issue.” IBM explained in its announcement that creating increasing numbers of logical qubits capable of executing quantum circuits with as few physical qubits as possible is critical to quantum computing at scale. Until today, a clear path to building such a fault-tolerant system without unrealistic engineering overhead has not been published. Alternative and previous gold-standard, error-correcting codes present fundamental engineering challenges, IBM continued. To scale, they would require an unfeasible number of physical qubits to create enough logical qubits to perform complex operations — necessitating impractical amounts of infrastructure and control electronics. This renders them unlikely to be implemented beyond small-scale experiments and devices. In two research papers released with its roadmap, IBM detailed how it will overcome the challenges of building the large-scale, fault-tolerant architecture needed for a quantum computer. One paper outlines the use of quantum low-density parity checkcodes to reduce physical qubit overhead. The other describes methods for decoding errors in real time using conventional computing. According to IBM, a practical fault-tolerant quantum architecture must: Suppress enough errors for useful algorithms to succeed Prepare and measure logical qubits during computation Apply universal instructions to logical qubits Decode measurements from logical qubits in real time and guide subsequent operations Scale modularly across hundreds or thousands of logical qubits Be efficient enough to run meaningful algorithms using realistic energy and infrastructure resources Aside from the technological challenges that quantum computer makers are facing, there may also be some market challenges. “Locating suitable use cases for quantum computers could be the biggest challenge,” Morningstar’s Yang maintained. “Only certain computing workloads, such as random circuit sampling, can fully unleash the computing power of quantum computers and show their advantage over the traditional supercomputers we have now,” he said. “However, workloads like RCS are not very commercially useful, and we believe commercial relevance is one of the key factors that determine the total market size for quantum computers.” Q-Day Approaching Faster Than Expected For years now, organizations have been told they need to prepare for “Q-Day” — the day a quantum computer will be able to crack all the encryption they use to keep their data secure. This IBM announcement suggests the window for action to protect data may be closing faster than many anticipated. “This absolutely adds urgency and credibility to the security expert guidance on post-quantum encryption being factored into their planning now,” said Dave Krauthamer, field CTO of QuSecure, maker of quantum-safe security solutions, in San Mateo, Calif. “IBM’s move to create a large-scale fault-tolerant quantum computer by 2029 is indicative of the timeline collapsing,” he told TechNewsWorld. “A fault-tolerant quantum computer of this magnitude could be well on the path to crack asymmetric ciphers sooner than anyone thinks.” “Security leaders need to take everything connected to post-quantum encryption as a serious measure and work it into their security plans now — not later,” he said. Roger Grimes, a defense evangelist with KnowBe4, a security awareness training provider in Clearwater, Fla., pointed out that IBM is just the latest in a surge of quantum companies announcing quickly forthcoming computational breakthroughs within a few years. “It leads to the question of whether the U.S. government’s original PQCpreparation date of 2030 is still a safe date,” he told TechNewsWorld. “It’s starting to feel a lot more risky for any company to wait until 2030 to be prepared against quantum attacks. It also flies in the face of the latest cybersecurity EOthat relaxed PQC preparation rules as compared to Biden’s last EO PQC standard order, which told U.S. agencies to transition to PQC ASAP.” “Most US companies are doing zero to prepare for Q-Day attacks,” he declared. “The latest executive order seems to tell U.S. agencies — and indirectly, all U.S. businesses — that they have more time to prepare. It’s going to cause even more agencies and businesses to be less prepared during a time when it seems multiple quantum computing companies are making significant progress.” “It definitely feels that something is going to give soon,” he said, “and if I were a betting man, and I am, I would bet that most U.S. companies are going to be unprepared for Q-Day on the day Q-Day becomes a reality.” John P. Mello Jr. has been an ECT News Network reporter since 2003. His areas of focus include cybersecurity, IT issues, privacy, e-commerce, social media, artificial intelligence, big data and consumer electronics. He has written and edited for numerous publications, including the Boston Business Journal, the Boston Phoenix, Megapixel.Net and Government Security News. Email John. Leave a Comment Click here to cancel reply. Please sign in to post or reply to a comment. New users create a free account. Related Stories More by John P. Mello Jr. view all More in Emerging Tech #ibm #plans #largescale #faulttolerant #quantum
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    IBM Plans Large-Scale Fault-Tolerant Quantum Computer by 2029
    IBM Plans Large-Scale Fault-Tolerant Quantum Computer by 2029 By John P. Mello Jr. June 11, 2025 5:00 AM PT IBM unveiled its plan to build IBM Quantum Starling, shown in this rendering. Starling is expected to be the first large-scale, fault-tolerant quantum system. (Image Credit: IBM) ADVERTISEMENT Enterprise IT Lead Generation Services Fuel Your Pipeline. Close More Deals. Our full-service marketing programs deliver sales-ready leads. 100% Satisfaction Guarantee! Learn more. IBM revealed Tuesday its roadmap for bringing a large-scale, fault-tolerant quantum computer, IBM Quantum Starling, online by 2029, which is significantly earlier than many technologists thought possible. The company predicts that when its new Starling computer is up and running, it will be capable of performing 20,000 times more operations than today’s quantum computers — a computational state so vast it would require the memory of more than a quindecillion (10⁴⁸) of the world’s most powerful supercomputers to represent. “IBM is charting the next frontier in quantum computing,” Big Blue CEO Arvind Krishna said in a statement. “Our expertise across mathematics, physics, and engineering is paving the way for a large-scale, fault-tolerant quantum computer — one that will solve real-world challenges and unlock immense possibilities for business.” IBM’s plan to deliver a fault-tolerant quantum system by 2029 is ambitious but not implausible, especially given the rapid pace of its quantum roadmap and past milestones, observed Ensar Seker, CISO at SOCRadar, a threat intelligence company in Newark, Del. “They’ve consistently met or exceeded their qubit scaling goals, and their emphasis on modularity and error correction indicates they’re tackling the right challenges,” he told TechNewsWorld. “However, moving from thousands to millions of physical qubits with sufficient fidelity remains a steep climb.” A qubit is the fundamental unit of information in quantum computing, capable of representing a zero, a one, or both simultaneously due to quantum superposition. In practice, fault-tolerant quantum computers use clusters of physical qubits working together to form a logical qubit — a more stable unit designed to store quantum information and correct errors in real time. Realistic Roadmap Luke Yang, an equity analyst with Morningstar Research Services in Chicago, believes IBM’s roadmap is realistic. “The exact scale and error correction performance might still change between now and 2029, but overall, the goal is reasonable,” he told TechNewsWorld. “Given its reliability and professionalism, IBM’s bold claim should be taken seriously,” said Enrique Solano, co-CEO and co-founder of Kipu Quantum, a quantum algorithm company with offices in Berlin and Karlsruhe, Germany. “Of course, it may also fail, especially when considering the unpredictability of hardware complexities involved,” he told TechNewsWorld, “but companies like IBM exist for such challenges, and we should all be positively impressed by its current achievements and promised technological roadmap.” Tim Hollebeek, vice president of industry standards at DigiCert, a global digital security company, added: “IBM is a leader in this area, and not normally a company that hypes their news. This is a fast-moving industry, and success is certainly possible.” “IBM is attempting to do something that no one has ever done before and will almost certainly run into challenges,” he told TechNewsWorld, “but at this point, it is largely an engineering scaling exercise, not a research project.” “IBM has demonstrated consistent progress, has committed $30 billion over five years to quantum computing, and the timeline is within the realm of technical feasibility,” noted John Young, COO of Quantum eMotion, a developer of quantum random number generator technology, in Saint-Laurent, Quebec, Canada. “That said,” he told TechNewsWorld, “fault-tolerant in a practical, industrial sense is a very high bar.” Solving the Quantum Error Correction Puzzle To make a quantum computer fault-tolerant, errors need to be corrected so large workloads can be run without faults. In a quantum computer, errors are reduced by clustering physical qubits to form logical qubits, which have lower error rates than the underlying physical qubits. “Error correction is a challenge,” Young said. “Logical qubits require thousands of physical qubits to function reliably. That’s a massive scaling issue.” IBM explained in its announcement that creating increasing numbers of logical qubits capable of executing quantum circuits with as few physical qubits as possible is critical to quantum computing at scale. Until today, a clear path to building such a fault-tolerant system without unrealistic engineering overhead has not been published. Alternative and previous gold-standard, error-correcting codes present fundamental engineering challenges, IBM continued. To scale, they would require an unfeasible number of physical qubits to create enough logical qubits to perform complex operations — necessitating impractical amounts of infrastructure and control electronics. This renders them unlikely to be implemented beyond small-scale experiments and devices. In two research papers released with its roadmap, IBM detailed how it will overcome the challenges of building the large-scale, fault-tolerant architecture needed for a quantum computer. One paper outlines the use of quantum low-density parity check (qLDPC) codes to reduce physical qubit overhead. The other describes methods for decoding errors in real time using conventional computing. According to IBM, a practical fault-tolerant quantum architecture must: Suppress enough errors for useful algorithms to succeed Prepare and measure logical qubits during computation Apply universal instructions to logical qubits Decode measurements from logical qubits in real time and guide subsequent operations Scale modularly across hundreds or thousands of logical qubits Be efficient enough to run meaningful algorithms using realistic energy and infrastructure resources Aside from the technological challenges that quantum computer makers are facing, there may also be some market challenges. “Locating suitable use cases for quantum computers could be the biggest challenge,” Morningstar’s Yang maintained. “Only certain computing workloads, such as random circuit sampling [RCS], can fully unleash the computing power of quantum computers and show their advantage over the traditional supercomputers we have now,” he said. “However, workloads like RCS are not very commercially useful, and we believe commercial relevance is one of the key factors that determine the total market size for quantum computers.” Q-Day Approaching Faster Than Expected For years now, organizations have been told they need to prepare for “Q-Day” — the day a quantum computer will be able to crack all the encryption they use to keep their data secure. This IBM announcement suggests the window for action to protect data may be closing faster than many anticipated. “This absolutely adds urgency and credibility to the security expert guidance on post-quantum encryption being factored into their planning now,” said Dave Krauthamer, field CTO of QuSecure, maker of quantum-safe security solutions, in San Mateo, Calif. “IBM’s move to create a large-scale fault-tolerant quantum computer by 2029 is indicative of the timeline collapsing,” he told TechNewsWorld. “A fault-tolerant quantum computer of this magnitude could be well on the path to crack asymmetric ciphers sooner than anyone thinks.” “Security leaders need to take everything connected to post-quantum encryption as a serious measure and work it into their security plans now — not later,” he said. Roger Grimes, a defense evangelist with KnowBe4, a security awareness training provider in Clearwater, Fla., pointed out that IBM is just the latest in a surge of quantum companies announcing quickly forthcoming computational breakthroughs within a few years. “It leads to the question of whether the U.S. government’s original PQC [post-quantum cryptography] preparation date of 2030 is still a safe date,” he told TechNewsWorld. “It’s starting to feel a lot more risky for any company to wait until 2030 to be prepared against quantum attacks. It also flies in the face of the latest cybersecurity EO [Executive Order] that relaxed PQC preparation rules as compared to Biden’s last EO PQC standard order, which told U.S. agencies to transition to PQC ASAP.” “Most US companies are doing zero to prepare for Q-Day attacks,” he declared. “The latest executive order seems to tell U.S. agencies — and indirectly, all U.S. businesses — that they have more time to prepare. It’s going to cause even more agencies and businesses to be less prepared during a time when it seems multiple quantum computing companies are making significant progress.” “It definitely feels that something is going to give soon,” he said, “and if I were a betting man, and I am, I would bet that most U.S. companies are going to be unprepared for Q-Day on the day Q-Day becomes a reality.” John P. Mello Jr. has been an ECT News Network reporter since 2003. His areas of focus include cybersecurity, IT issues, privacy, e-commerce, social media, artificial intelligence, big data and consumer electronics. He has written and edited for numerous publications, including the Boston Business Journal, the Boston Phoenix, Megapixel.Net and Government Security News. Email John. Leave a Comment Click here to cancel reply. Please sign in to post or reply to a comment. New users create a free account. Related Stories More by John P. Mello Jr. view all More in Emerging Tech
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  • CERT Director Greg Touhill: To Lead Is to Serve

    Greg Touhill, director of the Software Engineering’s Institute’sComputer Emergency Response Teamdivision 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 computersfor 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 Managementbreach. 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 million, Touhill and his team quickly architected, tested, and fielded the Radio over IP networkthat 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.” 
    #cert #director #greg #touhill #lead
    CERT Director Greg Touhill: To Lead Is to Serve
    Greg Touhill, director of the Software Engineering’s Institute’sComputer Emergency Response Teamdivision 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 computersfor 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 Managementbreach. 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 million, Touhill and his team quickly architected, tested, and fielded the Radio over IP networkthat 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.”  #cert #director #greg #touhill #lead
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    CERT Director Greg Touhill: To Lead Is to Serve
    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.” 
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