• LibreOffice Explains 'Real Costs' of Upgrading to Microsoft's Windows 11, Urges Taking Control with Linux

    KDE isn't the only organization reaching out to " as Microsoft prepares to end support for Windows 10.

    "Now, The Document Foundation, maker of LibreOffice, has also joined in to support the Endof10 initiative," reports the tech blog Neowin:
    The foundation writes: "You don't have to follow Microsoft's upgrade path. There is a better option that puts control back in the hands of users, institutions, and public bodies: Linux and LibreOffice. Together, these two programmes offer a powerful, privacy-friendly and future-proof alternative to the Windows + Microsoft 365 ecosystem."

    It further adds the "real costs" of upgrading to Windows 11 as it writes:

    "The move to Windows 11 isn't just about security updates. It increases dependence on Microsoft through aggressive cloud integration, forcing users to adopt Microsoft accounts and services. It also leads to higher costs due to subscription and licensing models, and reduces control over how your computer works and how your data is managed. Furthermore, new hardware requirements will render millions of perfectly good PCs obsolete.... The end of Windows 10 does not mark the end of choice, but the beginning of a new era. If you are tired of mandatory updates, invasive changes, and being bound by the commercial choices of a single supplier, it is time for a change. Linux and LibreOffice are ready — 2025 is the right year to choose digital freedom!"
    The first words on LibreOffice's announcement? "The countdown has begun...."

    of this story at Slashdot.
    #libreoffice #explains #039real #costs039 #upgrading
    LibreOffice Explains 'Real Costs' of Upgrading to Microsoft's Windows 11, Urges Taking Control with Linux
    KDE isn't the only organization reaching out to " as Microsoft prepares to end support for Windows 10. "Now, The Document Foundation, maker of LibreOffice, has also joined in to support the Endof10 initiative," reports the tech blog Neowin: The foundation writes: "You don't have to follow Microsoft's upgrade path. There is a better option that puts control back in the hands of users, institutions, and public bodies: Linux and LibreOffice. Together, these two programmes offer a powerful, privacy-friendly and future-proof alternative to the Windows + Microsoft 365 ecosystem." It further adds the "real costs" of upgrading to Windows 11 as it writes: "The move to Windows 11 isn't just about security updates. It increases dependence on Microsoft through aggressive cloud integration, forcing users to adopt Microsoft accounts and services. It also leads to higher costs due to subscription and licensing models, and reduces control over how your computer works and how your data is managed. Furthermore, new hardware requirements will render millions of perfectly good PCs obsolete.... The end of Windows 10 does not mark the end of choice, but the beginning of a new era. If you are tired of mandatory updates, invasive changes, and being bound by the commercial choices of a single supplier, it is time for a change. Linux and LibreOffice are ready — 2025 is the right year to choose digital freedom!" The first words on LibreOffice's announcement? "The countdown has begun...." of this story at Slashdot. #libreoffice #explains #039real #costs039 #upgrading
    TECH.SLASHDOT.ORG
    LibreOffice Explains 'Real Costs' of Upgrading to Microsoft's Windows 11, Urges Taking Control with Linux
    KDE isn't the only organization reaching out to " as Microsoft prepares to end support for Windows 10. "Now, The Document Foundation, maker of LibreOffice, has also joined in to support the Endof10 initiative," reports the tech blog Neowin: The foundation writes: "You don't have to follow Microsoft's upgrade path. There is a better option that puts control back in the hands of users, institutions, and public bodies: Linux and LibreOffice. Together, these two programmes offer a powerful, privacy-friendly and future-proof alternative to the Windows + Microsoft 365 ecosystem." It further adds the "real costs" of upgrading to Windows 11 as it writes: "The move to Windows 11 isn't just about security updates. It increases dependence on Microsoft through aggressive cloud integration, forcing users to adopt Microsoft accounts and services. It also leads to higher costs due to subscription and licensing models, and reduces control over how your computer works and how your data is managed. Furthermore, new hardware requirements will render millions of perfectly good PCs obsolete.... The end of Windows 10 does not mark the end of choice, but the beginning of a new era. If you are tired of mandatory updates, invasive changes, and being bound by the commercial choices of a single supplier, it is time for a change. Linux and LibreOffice are ready — 2025 is the right year to choose digital freedom!" The first words on LibreOffice's announcement? "The countdown has begun...." Read more of this story at Slashdot.
    Like
    Love
    Wow
    Sad
    Angry
    510
    0 Σχόλια 0 Μοιράστηκε
  • Microsoft 365 security in the spotlight after Washington Post hack

    When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works.

    Microsoft 365 security in the spotlight after Washington Post hack

    Paul Hill

    Neowin
    @ziks_99 ·

    Jun 16, 2025 03:36 EDT

    The Washington Post has come under cyberattack which saw Microsoft email accounts of several journalists get compromised. The attack, which was discovered last Thursday, is believed to have been conducted by a foreign government due to the topics the journalists cover, including national security, economic policy, and China. Following the hack, the passwords on the affected accounts were reset to prevent access.
    The fact that a Microsoft work email account was potentially hacked strongly suggests The Washington Post utilizes Microsoft 365, which makes us question the security of Microsoft’s widely used enterprise services. Given that Microsoft 365 is very popular, it is a hot target for attackers.
    Microsoft's enterprise security offerings and challenges

    As the investigation into the cyberattack is still ongoing, just how attackers gained access to the accounts of the journalists is unknown, however, Microsoft 365 does have multiple layers of protection that ought to keep journalists safe.
    One of the security tools is Microsoft Defender for Office 365. If the hackers tried to gain access with malicious links, Defender provides protection against any malicious attachments, links, or email-based phishing attempts with the Advanced Threat Protection feature. Defender also helps to protect against malware that could be used to target journalists at The Washington Post.
    Another security measure in place is Entra ID which helps enterprises defend against identity-based attacks. Some key features of Entra ID include multi-factor authentication which protects accounts even if a password is compromised, and there are granular access policies that help to limit logins from outside certain locations, unknown devices, or limit which apps can be used.
    While Microsoft does offer plenty of security technologies with M365, hacks can still take place due to misconfiguration, user-error, or through the exploitation of zero-day vulnerabilities. Essentially, it requires efforts from both Microsoft and the customer to maintain security.
    Lessons for organizations using Microsoft 365
    The incident over at The Washington Post serves as a stark reminder that all organizations, not just news organizations, should audit and strengthen their security setups. Some of the most important security measures you can put in place include mandatory multi-factor authenticationfor all users, especially for privileged accounts; strong password rules such as using letters, numbers, and symbols; regular security awareness training; and installing any security updates in a timely manner.
    Many of the cyberattacks that we learn about from companies like Microsoft involve hackers taking advantage of the human in the equation, such as being tricked into sharing passwords or sharing sensitive information due to trickery on behalf of the hackers. This highlights that employee training is crucial in protecting systems and that Microsoft’s technologies, as advanced as they are, can’t mitigate all attacks 100 percent of the time.

    Tags

    Report a problem with article

    Follow @NeowinFeed
    #microsoft #security #spotlight #after #washington
    Microsoft 365 security in the spotlight after Washington Post hack
    When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. Microsoft 365 security in the spotlight after Washington Post hack Paul Hill Neowin @ziks_99 · Jun 16, 2025 03:36 EDT The Washington Post has come under cyberattack which saw Microsoft email accounts of several journalists get compromised. The attack, which was discovered last Thursday, is believed to have been conducted by a foreign government due to the topics the journalists cover, including national security, economic policy, and China. Following the hack, the passwords on the affected accounts were reset to prevent access. The fact that a Microsoft work email account was potentially hacked strongly suggests The Washington Post utilizes Microsoft 365, which makes us question the security of Microsoft’s widely used enterprise services. Given that Microsoft 365 is very popular, it is a hot target for attackers. Microsoft's enterprise security offerings and challenges As the investigation into the cyberattack is still ongoing, just how attackers gained access to the accounts of the journalists is unknown, however, Microsoft 365 does have multiple layers of protection that ought to keep journalists safe. One of the security tools is Microsoft Defender for Office 365. If the hackers tried to gain access with malicious links, Defender provides protection against any malicious attachments, links, or email-based phishing attempts with the Advanced Threat Protection feature. Defender also helps to protect against malware that could be used to target journalists at The Washington Post. Another security measure in place is Entra ID which helps enterprises defend against identity-based attacks. Some key features of Entra ID include multi-factor authentication which protects accounts even if a password is compromised, and there are granular access policies that help to limit logins from outside certain locations, unknown devices, or limit which apps can be used. While Microsoft does offer plenty of security technologies with M365, hacks can still take place due to misconfiguration, user-error, or through the exploitation of zero-day vulnerabilities. Essentially, it requires efforts from both Microsoft and the customer to maintain security. Lessons for organizations using Microsoft 365 The incident over at The Washington Post serves as a stark reminder that all organizations, not just news organizations, should audit and strengthen their security setups. Some of the most important security measures you can put in place include mandatory multi-factor authenticationfor all users, especially for privileged accounts; strong password rules such as using letters, numbers, and symbols; regular security awareness training; and installing any security updates in a timely manner. Many of the cyberattacks that we learn about from companies like Microsoft involve hackers taking advantage of the human in the equation, such as being tricked into sharing passwords or sharing sensitive information due to trickery on behalf of the hackers. This highlights that employee training is crucial in protecting systems and that Microsoft’s technologies, as advanced as they are, can’t mitigate all attacks 100 percent of the time. Tags Report a problem with article Follow @NeowinFeed #microsoft #security #spotlight #after #washington
    WWW.NEOWIN.NET
    Microsoft 365 security in the spotlight after Washington Post hack
    When you purchase through links on our site, we may earn an affiliate commission. Here’s how it works. Microsoft 365 security in the spotlight after Washington Post hack Paul Hill Neowin @ziks_99 · Jun 16, 2025 03:36 EDT The Washington Post has come under cyberattack which saw Microsoft email accounts of several journalists get compromised. The attack, which was discovered last Thursday, is believed to have been conducted by a foreign government due to the topics the journalists cover, including national security, economic policy, and China. Following the hack, the passwords on the affected accounts were reset to prevent access. The fact that a Microsoft work email account was potentially hacked strongly suggests The Washington Post utilizes Microsoft 365, which makes us question the security of Microsoft’s widely used enterprise services. Given that Microsoft 365 is very popular, it is a hot target for attackers. Microsoft's enterprise security offerings and challenges As the investigation into the cyberattack is still ongoing, just how attackers gained access to the accounts of the journalists is unknown, however, Microsoft 365 does have multiple layers of protection that ought to keep journalists safe. One of the security tools is Microsoft Defender for Office 365. If the hackers tried to gain access with malicious links, Defender provides protection against any malicious attachments, links, or email-based phishing attempts with the Advanced Threat Protection feature. Defender also helps to protect against malware that could be used to target journalists at The Washington Post. Another security measure in place is Entra ID which helps enterprises defend against identity-based attacks. Some key features of Entra ID include multi-factor authentication which protects accounts even if a password is compromised, and there are granular access policies that help to limit logins from outside certain locations, unknown devices, or limit which apps can be used. While Microsoft does offer plenty of security technologies with M365, hacks can still take place due to misconfiguration, user-error, or through the exploitation of zero-day vulnerabilities. Essentially, it requires efforts from both Microsoft and the customer to maintain security. Lessons for organizations using Microsoft 365 The incident over at The Washington Post serves as a stark reminder that all organizations, not just news organizations, should audit and strengthen their security setups. Some of the most important security measures you can put in place include mandatory multi-factor authentication (MFA) for all users, especially for privileged accounts; strong password rules such as using letters, numbers, and symbols; regular security awareness training; and installing any security updates in a timely manner. Many of the cyberattacks that we learn about from companies like Microsoft involve hackers taking advantage of the human in the equation, such as being tricked into sharing passwords or sharing sensitive information due to trickery on behalf of the hackers. This highlights that employee training is crucial in protecting systems and that Microsoft’s technologies, as advanced as they are, can’t mitigate all attacks 100 percent of the time. Tags Report a problem with article Follow @NeowinFeed
    Like
    Love
    Wow
    Sad
    Angry
    553
    0 Σχόλια 0 Μοιράστηκε
  • Over 8M patient records leaked in healthcare data breach

    Published
    June 15, 2025 10:00am EDT close IPhone users instructed to take immediate action to avoid data breach: 'Urgent threat' Kurt 'The CyberGuy' Knutsson discusses Elon Musk's possible priorities as he exits his role with the White House and explains the urgent warning for iPhone users to update devices after a 'massive security gap.' NEWYou can now listen to Fox News articles!
    In the past decade, healthcare data has become one of the most sought-after targets in cybercrime. From insurers to clinics, every player in the ecosystem handles some form of sensitive information. However, breaches do not always originate from hospitals or health apps. Increasingly, patient data is managed by third-party vendors offering digital services such as scheduling, billing and marketing. One such breach at a digital marketing agency serving dental practices recently exposed approximately 2.7 million patient profiles and more than 8.8 million appointment records.Sign up for my FREE CyberGuy ReportGet my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you’ll get instant access to my Ultimate Scam Survival Guide — free when you join. Illustration of a hacker at work  Massive healthcare data leak exposes millions: What you need to knowCybernews researchers have discovered a misconfigured MongoDB database exposing 2.7 million patient profiles and 8.8 million appointment records. The database was publicly accessible online, unprotected by passwords or authentication protocols. Anyone with basic knowledge of database scanning tools could have accessed it.The exposed data included names, birthdates, addresses, emails, phone numbers, gender, chart IDs, language preferences and billing classifications. Appointment records also contained metadata such as timestamps and institutional identifiers.MASSIVE DATA BREACH EXPOSES 184 MILLION PASSWORDS AND LOGINSClues within the data structure point toward Gargle, a Utah-based company that builds websites and offers marketing tools for dental practices. While not a confirmed source, several internal references and system details suggest a strong connection. Gargle provides appointment scheduling, form submission and patient communication services. These functions require access to patient information, making the firm a likely link in the exposure.After the issue was reported, the database was secured. The duration of the exposure remains unknown, and there is no public evidence indicating whether the data was downloaded by malicious actors before being locked down.We reached out to Gargle for a comment but did not hear back before our deadline. A healthcare professional viewing heath data     How healthcare data breaches lead to identity theft and insurance fraudThe exposed data presents a broad risk profile. On its own, a phone number or billing record might seem limited in scope. Combined, however, the dataset forms a complete profile that could be exploited for identity theft, insurance fraud and targeted phishing campaigns.Medical identity theft allows attackers to impersonate patients and access services under a false identity. Victims often remain unaware until significant damage is done, ranging from incorrect medical records to unpaid bills in their names. The leak also opens the door to insurance fraud, with actors using institutional references and chart data to submit false claims.This type of breach raises questions about compliance with the Health Insurance Portability and Accountability Act, which mandates strong security protections for entities handling patient data. Although Gargle is not a healthcare provider, its access to patient-facing infrastructure could place it under the scope of that regulation as a business associate. A healthcare professional working on a laptop  5 ways you can stay safe from healthcare data breachesIf your information was part of the healthcare breach or any similar one, it’s worth taking a few steps to protect yourself.1. Consider identity theft protection services: Since the healthcare data breach exposed personal and financial information, it’s crucial to stay proactive against identity theft. Identity theft protection services offer continuous monitoring of your credit reports, Social Security number and even the dark web to detect if your information is being misused. These services send you real-time alerts about suspicious activity, such as new credit inquiries or attempts to open accounts in your name, helping you act quickly before serious damage occurs. Beyond monitoring, many identity theft protection companies provide dedicated recovery specialists who assist you in resolving fraud issues, disputing unauthorized charges and restoring your identity if it’s compromised. See my tips and best picks on how to protect yourself from identity theft.2. Use personal data removal services: The healthcare data breach leaks loads of information about you, and all this could end up in the public domain, which essentially gives anyone an opportunity to scam you.  One proactive step is to consider personal data removal services, which specialize in continuously monitoring and removing your information from various online databases and websites. While no service promises to remove all your data from the internet, having a removal service is great if you want to constantly monitor and automate the process of removing your information from hundreds of sites continuously over a longer period of time. Check out my top picks for data removal services here. GET FOX BUSINESS ON THE GO BY CLICKING HEREGet a free scan to find out if your personal information is already out on the web3. Have strong antivirus software: Hackers have people’s email addresses and full names, which makes it easy for them to send you a phishing link that installs malware and steals all your data. These messages are socially engineered to catch them, and catching them is nearly impossible if you’re not careful. However, you’re not without defenses.The best way to safeguard yourself from malicious links that install malware, potentially accessing your private information, is to have strong antivirus software installed on all your devices. This protection can also alert you to phishing emails and ransomware scams, keeping your personal information and digital assets safe. Get my picks for the best 2025 antivirus protection winners for your Windows, Mac, Android and iOS devices.4. Enable two-factor authentication: While passwords weren’t part of the data breach, you still need to enable two-factor authentication. It gives you an extra layer of security on all your important accounts, including email, banking and social media. 2FA requires you to provide a second piece of information, such as a code sent to your phone, in addition to your password when logging in. This makes it significantly harder for hackers to access your accounts, even if they have your password. Enabling 2FA can greatly reduce the risk of unauthorized access and protect your sensitive data.5. Be wary of mailbox communications: Bad actors may also try to scam you through snail mail. The data leak gives them access to your address. They may impersonate people or brands you know and use themes that require urgent attention, such as missed deliveries, account suspensions and security alerts. Kurt’s key takeawayIf nothing else, this latest leak shows just how poorly patient data is being handled today. More and more, non-medical vendors are getting access to sensitive information without facing the same rules or oversight as hospitals and clinics. These third-party services are now a regular part of how patients book appointments, pay bills or fill out forms. But when something goes wrong, the fallout is just as serious. Even though the database was taken offline, the bigger problem hasn't gone away. Your data is only as safe as the least careful company that gets access to it.CLICK HERE TO GET THE FOX NEWS APPDo you think healthcare companies are investing enough in their cybersecurity infrastructure? Let us know by writing us at Cyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to coverFollow Kurt on his social channelsAnswers to the most asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com.  All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.
    #over #patient #records #leaked #healthcare
    Over 8M patient records leaked in healthcare data breach
    Published June 15, 2025 10:00am EDT close IPhone users instructed to take immediate action to avoid data breach: 'Urgent threat' Kurt 'The CyberGuy' Knutsson discusses Elon Musk's possible priorities as he exits his role with the White House and explains the urgent warning for iPhone users to update devices after a 'massive security gap.' NEWYou can now listen to Fox News articles! In the past decade, healthcare data has become one of the most sought-after targets in cybercrime. From insurers to clinics, every player in the ecosystem handles some form of sensitive information. However, breaches do not always originate from hospitals or health apps. Increasingly, patient data is managed by third-party vendors offering digital services such as scheduling, billing and marketing. One such breach at a digital marketing agency serving dental practices recently exposed approximately 2.7 million patient profiles and more than 8.8 million appointment records.Sign up for my FREE CyberGuy ReportGet my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you’ll get instant access to my Ultimate Scam Survival Guide — free when you join. Illustration of a hacker at work  Massive healthcare data leak exposes millions: What you need to knowCybernews researchers have discovered a misconfigured MongoDB database exposing 2.7 million patient profiles and 8.8 million appointment records. The database was publicly accessible online, unprotected by passwords or authentication protocols. Anyone with basic knowledge of database scanning tools could have accessed it.The exposed data included names, birthdates, addresses, emails, phone numbers, gender, chart IDs, language preferences and billing classifications. Appointment records also contained metadata such as timestamps and institutional identifiers.MASSIVE DATA BREACH EXPOSES 184 MILLION PASSWORDS AND LOGINSClues within the data structure point toward Gargle, a Utah-based company that builds websites and offers marketing tools for dental practices. While not a confirmed source, several internal references and system details suggest a strong connection. Gargle provides appointment scheduling, form submission and patient communication services. These functions require access to patient information, making the firm a likely link in the exposure.After the issue was reported, the database was secured. The duration of the exposure remains unknown, and there is no public evidence indicating whether the data was downloaded by malicious actors before being locked down.We reached out to Gargle for a comment but did not hear back before our deadline. A healthcare professional viewing heath data     How healthcare data breaches lead to identity theft and insurance fraudThe exposed data presents a broad risk profile. On its own, a phone number or billing record might seem limited in scope. Combined, however, the dataset forms a complete profile that could be exploited for identity theft, insurance fraud and targeted phishing campaigns.Medical identity theft allows attackers to impersonate patients and access services under a false identity. Victims often remain unaware until significant damage is done, ranging from incorrect medical records to unpaid bills in their names. The leak also opens the door to insurance fraud, with actors using institutional references and chart data to submit false claims.This type of breach raises questions about compliance with the Health Insurance Portability and Accountability Act, which mandates strong security protections for entities handling patient data. Although Gargle is not a healthcare provider, its access to patient-facing infrastructure could place it under the scope of that regulation as a business associate. A healthcare professional working on a laptop  5 ways you can stay safe from healthcare data breachesIf your information was part of the healthcare breach or any similar one, it’s worth taking a few steps to protect yourself.1. Consider identity theft protection services: Since the healthcare data breach exposed personal and financial information, it’s crucial to stay proactive against identity theft. Identity theft protection services offer continuous monitoring of your credit reports, Social Security number and even the dark web to detect if your information is being misused. These services send you real-time alerts about suspicious activity, such as new credit inquiries or attempts to open accounts in your name, helping you act quickly before serious damage occurs. Beyond monitoring, many identity theft protection companies provide dedicated recovery specialists who assist you in resolving fraud issues, disputing unauthorized charges and restoring your identity if it’s compromised. See my tips and best picks on how to protect yourself from identity theft.2. Use personal data removal services: The healthcare data breach leaks loads of information about you, and all this could end up in the public domain, which essentially gives anyone an opportunity to scam you.  One proactive step is to consider personal data removal services, which specialize in continuously monitoring and removing your information from various online databases and websites. While no service promises to remove all your data from the internet, having a removal service is great if you want to constantly monitor and automate the process of removing your information from hundreds of sites continuously over a longer period of time. Check out my top picks for data removal services here. GET FOX BUSINESS ON THE GO BY CLICKING HEREGet a free scan to find out if your personal information is already out on the web3. Have strong antivirus software: Hackers have people’s email addresses and full names, which makes it easy for them to send you a phishing link that installs malware and steals all your data. These messages are socially engineered to catch them, and catching them is nearly impossible if you’re not careful. However, you’re not without defenses.The best way to safeguard yourself from malicious links that install malware, potentially accessing your private information, is to have strong antivirus software installed on all your devices. This protection can also alert you to phishing emails and ransomware scams, keeping your personal information and digital assets safe. Get my picks for the best 2025 antivirus protection winners for your Windows, Mac, Android and iOS devices.4. Enable two-factor authentication: While passwords weren’t part of the data breach, you still need to enable two-factor authentication. It gives you an extra layer of security on all your important accounts, including email, banking and social media. 2FA requires you to provide a second piece of information, such as a code sent to your phone, in addition to your password when logging in. This makes it significantly harder for hackers to access your accounts, even if they have your password. Enabling 2FA can greatly reduce the risk of unauthorized access and protect your sensitive data.5. Be wary of mailbox communications: Bad actors may also try to scam you through snail mail. The data leak gives them access to your address. They may impersonate people or brands you know and use themes that require urgent attention, such as missed deliveries, account suspensions and security alerts. Kurt’s key takeawayIf nothing else, this latest leak shows just how poorly patient data is being handled today. More and more, non-medical vendors are getting access to sensitive information without facing the same rules or oversight as hospitals and clinics. These third-party services are now a regular part of how patients book appointments, pay bills or fill out forms. But when something goes wrong, the fallout is just as serious. Even though the database was taken offline, the bigger problem hasn't gone away. Your data is only as safe as the least careful company that gets access to it.CLICK HERE TO GET THE FOX NEWS APPDo you think healthcare companies are investing enough in their cybersecurity infrastructure? Let us know by writing us at Cyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to coverFollow Kurt on his social channelsAnswers to the most asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com.  All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com. #over #patient #records #leaked #healthcare
    WWW.FOXNEWS.COM
    Over 8M patient records leaked in healthcare data breach
    Published June 15, 2025 10:00am EDT close IPhone users instructed to take immediate action to avoid data breach: 'Urgent threat' Kurt 'The CyberGuy' Knutsson discusses Elon Musk's possible priorities as he exits his role with the White House and explains the urgent warning for iPhone users to update devices after a 'massive security gap.' NEWYou can now listen to Fox News articles! In the past decade, healthcare data has become one of the most sought-after targets in cybercrime. From insurers to clinics, every player in the ecosystem handles some form of sensitive information. However, breaches do not always originate from hospitals or health apps. Increasingly, patient data is managed by third-party vendors offering digital services such as scheduling, billing and marketing. One such breach at a digital marketing agency serving dental practices recently exposed approximately 2.7 million patient profiles and more than 8.8 million appointment records.Sign up for my FREE CyberGuy ReportGet my best tech tips, urgent security alerts, and exclusive deals delivered straight to your inbox. Plus, you’ll get instant access to my Ultimate Scam Survival Guide — free when you join. Illustration of a hacker at work   (Kurt "CyberGuy" Knutsson)Massive healthcare data leak exposes millions: What you need to knowCybernews researchers have discovered a misconfigured MongoDB database exposing 2.7 million patient profiles and 8.8 million appointment records. The database was publicly accessible online, unprotected by passwords or authentication protocols. Anyone with basic knowledge of database scanning tools could have accessed it.The exposed data included names, birthdates, addresses, emails, phone numbers, gender, chart IDs, language preferences and billing classifications. Appointment records also contained metadata such as timestamps and institutional identifiers.MASSIVE DATA BREACH EXPOSES 184 MILLION PASSWORDS AND LOGINSClues within the data structure point toward Gargle, a Utah-based company that builds websites and offers marketing tools for dental practices. While not a confirmed source, several internal references and system details suggest a strong connection. Gargle provides appointment scheduling, form submission and patient communication services. These functions require access to patient information, making the firm a likely link in the exposure.After the issue was reported, the database was secured. The duration of the exposure remains unknown, and there is no public evidence indicating whether the data was downloaded by malicious actors before being locked down.We reached out to Gargle for a comment but did not hear back before our deadline. A healthcare professional viewing heath data      (Kurt "CyberGuy" Knutsson)How healthcare data breaches lead to identity theft and insurance fraudThe exposed data presents a broad risk profile. On its own, a phone number or billing record might seem limited in scope. Combined, however, the dataset forms a complete profile that could be exploited for identity theft, insurance fraud and targeted phishing campaigns.Medical identity theft allows attackers to impersonate patients and access services under a false identity. Victims often remain unaware until significant damage is done, ranging from incorrect medical records to unpaid bills in their names. The leak also opens the door to insurance fraud, with actors using institutional references and chart data to submit false claims.This type of breach raises questions about compliance with the Health Insurance Portability and Accountability Act, which mandates strong security protections for entities handling patient data. Although Gargle is not a healthcare provider, its access to patient-facing infrastructure could place it under the scope of that regulation as a business associate. A healthcare professional working on a laptop   (Kurt "CyberGuy" Knutsson)5 ways you can stay safe from healthcare data breachesIf your information was part of the healthcare breach or any similar one, it’s worth taking a few steps to protect yourself.1. Consider identity theft protection services: Since the healthcare data breach exposed personal and financial information, it’s crucial to stay proactive against identity theft. Identity theft protection services offer continuous monitoring of your credit reports, Social Security number and even the dark web to detect if your information is being misused. These services send you real-time alerts about suspicious activity, such as new credit inquiries or attempts to open accounts in your name, helping you act quickly before serious damage occurs. Beyond monitoring, many identity theft protection companies provide dedicated recovery specialists who assist you in resolving fraud issues, disputing unauthorized charges and restoring your identity if it’s compromised. See my tips and best picks on how to protect yourself from identity theft.2. Use personal data removal services: The healthcare data breach leaks loads of information about you, and all this could end up in the public domain, which essentially gives anyone an opportunity to scam you.  One proactive step is to consider personal data removal services, which specialize in continuously monitoring and removing your information from various online databases and websites. While no service promises to remove all your data from the internet, having a removal service is great if you want to constantly monitor and automate the process of removing your information from hundreds of sites continuously over a longer period of time. Check out my top picks for data removal services here. GET FOX BUSINESS ON THE GO BY CLICKING HEREGet a free scan to find out if your personal information is already out on the web3. Have strong antivirus software: Hackers have people’s email addresses and full names, which makes it easy for them to send you a phishing link that installs malware and steals all your data. These messages are socially engineered to catch them, and catching them is nearly impossible if you’re not careful. However, you’re not without defenses.The best way to safeguard yourself from malicious links that install malware, potentially accessing your private information, is to have strong antivirus software installed on all your devices. This protection can also alert you to phishing emails and ransomware scams, keeping your personal information and digital assets safe. Get my picks for the best 2025 antivirus protection winners for your Windows, Mac, Android and iOS devices.4. Enable two-factor authentication: While passwords weren’t part of the data breach, you still need to enable two-factor authentication (2FA). It gives you an extra layer of security on all your important accounts, including email, banking and social media. 2FA requires you to provide a second piece of information, such as a code sent to your phone, in addition to your password when logging in. This makes it significantly harder for hackers to access your accounts, even if they have your password. Enabling 2FA can greatly reduce the risk of unauthorized access and protect your sensitive data.5. Be wary of mailbox communications: Bad actors may also try to scam you through snail mail. The data leak gives them access to your address. They may impersonate people or brands you know and use themes that require urgent attention, such as missed deliveries, account suspensions and security alerts. Kurt’s key takeawayIf nothing else, this latest leak shows just how poorly patient data is being handled today. More and more, non-medical vendors are getting access to sensitive information without facing the same rules or oversight as hospitals and clinics. These third-party services are now a regular part of how patients book appointments, pay bills or fill out forms. But when something goes wrong, the fallout is just as serious. Even though the database was taken offline, the bigger problem hasn't gone away. Your data is only as safe as the least careful company that gets access to it.CLICK HERE TO GET THE FOX NEWS APPDo you think healthcare companies are investing enough in their cybersecurity infrastructure? Let us know by writing us at Cyberguy.com/ContactFor more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to coverFollow Kurt on his social channelsAnswers to the most asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com.  All rights reserved.   Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.
    Like
    Love
    Wow
    Sad
    Angry
    507
    0 Σχόλια 0 Μοιράστηκε
  • Aspora gets $50M from Sequioa to build remittance and banking solutions for Indian diaspora

    India has been one of the top recipients of remittances in the world for more than a decade. Inward remittances jumped from billion in 2010-11 to billion in 2023-24, according to data from the country’s central bank. The bank projects that figure will reach billion in 2029.
    This means there is an increasing market for digitalized banking experiences for non-resident Indians, ranging from remittances to investing in different assets back home.
    Asporais trying to build a verticalized financial experience for the Indian diaspora by keeping convenience at the center. While a lot of financial products are in its future roadmap, the company currently focuses largely on remittances.
    “While multiple financial products for non-resident Indians exist, they don’t know about them because there is no digital journey for them. They possibly use the same banking app as residents, which makes it harder for them to discover products catered towards them,” Garg said.
    In the last year, the company has grown the volume of remittances by 6x — from million to billion in yearly volume processed.
    With this growth, the company has attracted a lot of investor interest. It raised million in Series A funding last December — which was previously unreported — led by Sequoia with participation from Greylock, Y Combinator, Hummingbird Ventures, and Global Founders Capital. The round pegged the company’s valuation at million. In the four months following, the company tripled its transaction volume, prompting investors to put in more money.
    The company announced today it has raised million in Series B funding, co-led by Sequoia and Greylock, with Hummingbird, Quantum Light Ventures, and Y Combinator also contributing to the round. The startup said this round values the company at million. The startup has raised over million in funding to date.

    Techcrunch event

    + on your TechCrunch All Stage pass
    Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections.

    + on your TechCrunch All Stage pass
    Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections.

    Boston, MA
    |
    July 15

    REGISTER NOW

    After pivoting from being Pipe.com for India, the company started by offering remittance for NRIs in the U.K. in 2023 and has expanded its presence in other markets, including Europe and the United Arab Emirates. It charges a flat fee for money transfer and offers a competitive rate. Now it also allows customers to invest in mutual funds in India. The startup markets its exchange rates as “Google rate” as customers often search for currency conversion rates, even though they may not reflect live rates.
    The startup is also set to launch in the U.S., one of the biggest remittance corridors to India, next month. Plus, it plans to open up shop in Canada, Singapore, and Australia by the fourth quarter of this year.
    Garg, who grew up in the UAE, said that remittances are just the start, and the company wants to build out more financial tools for NRIs.
    “We want to use remittances as a wedge and build all the financial solutions that the diaspora needs, including banking, investing, insurance, lending in the home country, and products that help them take care of their parents,” he told TechCrunch.
    He added that a large chunk of money that NRIs send home is for wealth creation rather than family sustenance. The startup said that 80% of its users are sending money to their own accounts back home.
    In the next few months, the company is launching a few products to offer more services. This month, it plans to launch a bill payment platform to let users pay for services like rent and utilities. Next month, it plans to launch fixed deposit accounts for non-resident Indians that allow them to park money in foreign currency. By the end of the year, it plans to launch a full-stack banking account for NRIs that typically takes days for users to open. While these accounts can help the diaspora maintain their tax status in India, a lot of people use a family member’s account because of the cumbersome process, and Aspora wants to simplify this.
    Apart from banking, the company also plans to launch a product that would help NRIs take care of their parents back home by offering regular medical checkups, emergency care coverage, and concierge services for other assistance.
    Besides global competitors like Remittly and Wise, the company also has India-based rivals like Abound, which was spun off from Times Internet.
    Sequoia’s Luciana Lixandru is confident that Aspora’s execution speed and verticalized solution will give it an edge.
    “Speed of execution, for me, is one of the main indicators in the early days of the future success of a company,” she told TechCrunch over a call. “Aspora moves fast, but it is also very deliberate in building corridor by corridor, which is very important in financial services.”
    #aspora #gets #50m #sequioa #build
    Aspora gets $50M from Sequioa to build remittance and banking solutions for Indian diaspora
    India has been one of the top recipients of remittances in the world for more than a decade. Inward remittances jumped from billion in 2010-11 to billion in 2023-24, according to data from the country’s central bank. The bank projects that figure will reach billion in 2029. This means there is an increasing market for digitalized banking experiences for non-resident Indians, ranging from remittances to investing in different assets back home. Asporais trying to build a verticalized financial experience for the Indian diaspora by keeping convenience at the center. While a lot of financial products are in its future roadmap, the company currently focuses largely on remittances. “While multiple financial products for non-resident Indians exist, they don’t know about them because there is no digital journey for them. They possibly use the same banking app as residents, which makes it harder for them to discover products catered towards them,” Garg said. In the last year, the company has grown the volume of remittances by 6x — from million to billion in yearly volume processed. With this growth, the company has attracted a lot of investor interest. It raised million in Series A funding last December — which was previously unreported — led by Sequoia with participation from Greylock, Y Combinator, Hummingbird Ventures, and Global Founders Capital. The round pegged the company’s valuation at million. In the four months following, the company tripled its transaction volume, prompting investors to put in more money. The company announced today it has raised million in Series B funding, co-led by Sequoia and Greylock, with Hummingbird, Quantum Light Ventures, and Y Combinator also contributing to the round. The startup said this round values the company at million. The startup has raised over million in funding to date. Techcrunch event + on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. + on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | July 15 REGISTER NOW After pivoting from being Pipe.com for India, the company started by offering remittance for NRIs in the U.K. in 2023 and has expanded its presence in other markets, including Europe and the United Arab Emirates. It charges a flat fee for money transfer and offers a competitive rate. Now it also allows customers to invest in mutual funds in India. The startup markets its exchange rates as “Google rate” as customers often search for currency conversion rates, even though they may not reflect live rates. The startup is also set to launch in the U.S., one of the biggest remittance corridors to India, next month. Plus, it plans to open up shop in Canada, Singapore, and Australia by the fourth quarter of this year. Garg, who grew up in the UAE, said that remittances are just the start, and the company wants to build out more financial tools for NRIs. “We want to use remittances as a wedge and build all the financial solutions that the diaspora needs, including banking, investing, insurance, lending in the home country, and products that help them take care of their parents,” he told TechCrunch. He added that a large chunk of money that NRIs send home is for wealth creation rather than family sustenance. The startup said that 80% of its users are sending money to their own accounts back home. In the next few months, the company is launching a few products to offer more services. This month, it plans to launch a bill payment platform to let users pay for services like rent and utilities. Next month, it plans to launch fixed deposit accounts for non-resident Indians that allow them to park money in foreign currency. By the end of the year, it plans to launch a full-stack banking account for NRIs that typically takes days for users to open. While these accounts can help the diaspora maintain their tax status in India, a lot of people use a family member’s account because of the cumbersome process, and Aspora wants to simplify this. Apart from banking, the company also plans to launch a product that would help NRIs take care of their parents back home by offering regular medical checkups, emergency care coverage, and concierge services for other assistance. Besides global competitors like Remittly and Wise, the company also has India-based rivals like Abound, which was spun off from Times Internet. Sequoia’s Luciana Lixandru is confident that Aspora’s execution speed and verticalized solution will give it an edge. “Speed of execution, for me, is one of the main indicators in the early days of the future success of a company,” she told TechCrunch over a call. “Aspora moves fast, but it is also very deliberate in building corridor by corridor, which is very important in financial services.” #aspora #gets #50m #sequioa #build
    TECHCRUNCH.COM
    Aspora gets $50M from Sequioa to build remittance and banking solutions for Indian diaspora
    India has been one of the top recipients of remittances in the world for more than a decade. Inward remittances jumped from $55.6 billion in 2010-11 to $118.7 billion in 2023-24, according to data from the country’s central bank. The bank projects that figure will reach $160 billion in 2029. This means there is an increasing market for digitalized banking experiences for non-resident Indians(NRIs), ranging from remittances to investing in different assets back home. Aspora (formerly Vance) is trying to build a verticalized financial experience for the Indian diaspora by keeping convenience at the center. While a lot of financial products are in its future roadmap, the company currently focuses largely on remittances. “While multiple financial products for non-resident Indians exist, they don’t know about them because there is no digital journey for them. They possibly use the same banking app as residents, which makes it harder for them to discover products catered towards them,” Garg said. In the last year, the company has grown the volume of remittances by 6x — from $400 million to $2 billion in yearly volume processed. With this growth, the company has attracted a lot of investor interest. It raised $35 million in Series A funding last December — which was previously unreported — led by Sequoia with participation from Greylock, Y Combinator, Hummingbird Ventures, and Global Founders Capital. The round pegged the company’s valuation at $150 million. In the four months following, the company tripled its transaction volume, prompting investors to put in more money. The company announced today it has raised $50 million in Series B funding, co-led by Sequoia and Greylock, with Hummingbird, Quantum Light Ventures, and Y Combinator also contributing to the round. The startup said this round values the company at $500 million. The startup has raised over $99 million in funding to date. Techcrunch event Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Save $200+ on your TechCrunch All Stage pass Build smarter. Scale faster. Connect deeper. Join visionaries from Precursor Ventures, NEA, Index Ventures, Underscore VC, and beyond for a day packed with strategies, workshops, and meaningful connections. Boston, MA | July 15 REGISTER NOW After pivoting from being Pipe.com for India, the company started by offering remittance for NRIs in the U.K. in 2023 and has expanded its presence in other markets, including Europe and the United Arab Emirates. It charges a flat fee for money transfer and offers a competitive rate. Now it also allows customers to invest in mutual funds in India. The startup markets its exchange rates as “Google rate” as customers often search for currency conversion rates, even though they may not reflect live rates. The startup is also set to launch in the U.S., one of the biggest remittance corridors to India, next month. Plus, it plans to open up shop in Canada, Singapore, and Australia by the fourth quarter of this year. Garg, who grew up in the UAE, said that remittances are just the start, and the company wants to build out more financial tools for NRIs. “We want to use remittances as a wedge and build all the financial solutions that the diaspora needs, including banking, investing, insurance, lending in the home country, and products that help them take care of their parents,” he told TechCrunch. He added that a large chunk of money that NRIs send home is for wealth creation rather than family sustenance. The startup said that 80% of its users are sending money to their own accounts back home. In the next few months, the company is launching a few products to offer more services. This month, it plans to launch a bill payment platform to let users pay for services like rent and utilities. Next month, it plans to launch fixed deposit accounts for non-resident Indians that allow them to park money in foreign currency. By the end of the year, it plans to launch a full-stack banking account for NRIs that typically takes days for users to open. While these accounts can help the diaspora maintain their tax status in India, a lot of people use a family member’s account because of the cumbersome process, and Aspora wants to simplify this. Apart from banking, the company also plans to launch a product that would help NRIs take care of their parents back home by offering regular medical checkups, emergency care coverage, and concierge services for other assistance. Besides global competitors like Remittly and Wise, the company also has India-based rivals like Abound, which was spun off from Times Internet. Sequoia’s Luciana Lixandru is confident that Aspora’s execution speed and verticalized solution will give it an edge. “Speed of execution, for me, is one of the main indicators in the early days of the future success of a company,” she told TechCrunch over a call. “Aspora moves fast, but it is also very deliberate in building corridor by corridor, which is very important in financial services.”
    Like
    Love
    Wow
    Sad
    Angry
    514
    2 Σχόλια 0 Μοιράστηκε
  • Block’s CFO explains Gen Z’s surprising approach to money management

    One stock recently impacted by a whirlwind of volatility is Block—the fintech powerhouse behind Square, Cash App, Tidal Music, and more. The company’s COO and CFO, Amrita Ahuja, shares how her team is using new AI tools to find opportunity amid disruption and reach customers left behind by traditional financial systems. Ahuja also shares lessons from the video game industry and discusses Gen Z’s surprising approach to money management.  

    This is an abridged transcript of an interview from Rapid Response, hosted by Robert Safian, former editor-in-chief of Fast Company. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode.

    As a leader, when you’re looking at all of this volatility—the tariffs, consumer sentiment’s been unclear, the stock market’s been all over the place. You guys had a huge one-day drop in early May, and it quickly bounced back. How do you make sense of all these external factors?

    Yeah, our focus is on what we can control. And ultimately, the thing that we are laser-focused on for our business is product velocity. How quickly can we start small with something, launch something for our customers, and then test and iterate and learn so that ultimately, that something that we’ve launched scales into an important product?

    I’ll give you an example. Cash App Borrow, which is a product where our customers can get access to a line of credit, often that bridges them from paycheck to paycheck. We know so many Americans are living paycheck to paycheck. That’s a product that we launched about three years ago and have now scaled to serve 9 million actives with billion in credit supply to our customers in a span of a couple short years.

    The more we can be out testing and launching product at a pace, the more we know we are ultimately delivering value to our customers, and the right things will happen from a stock perspective.

    Block is a financial services provider. You have Square, the point-of-sale system; the digital wallet Cash App, which you mentioned, which competes with Venmo and Robinhood; and a bunch of others. Then you’ve got the buy-now, pay-later leader Afterpay. You chair Square Financial Services, which is Block’s chartered bank. But you’ve said that in the fintech world, Block is only a little bit fin—that comparatively, it’s more tech. Can you explain what you mean by that?

    What we think is unique about us is our ability as a technology company to completely change innovation in the space, such that we can help solve systemic issues across credit, payments, commerce, and banking. What that means ultimately is we use technologies like AI and machine learning and data science, and we use these technologies in a unique way, in a way that’s different from a traditional bank. We are able to underwrite those who are often frankly forgotten by the traditional financial ecosystems.

    Our Square Loans product has almost triple the rate of women-owned businesses that we underwrite. Fifty-eight percent of our loans go to women-owned businesses versus 20% for the industry average. For that Cash App Borrow product I was talking about, 70% of those actives, the 9 million actives that we underwrote, fell below 580 as a FICO score. That’s considered a poor FICO score, and yet 97% of repayments are made on time. And this is because we have unique access to data and these technology and tools which can help us uniquely underwrite this often forgotten customer base.

    Yeah. I mean, credit—sometimes it’s been blamed for financial excesses. But access to credit is also, as you say, an advantage that’s not available to everyone. Do you have a philosophy between those poles—between risk and opportunity? Or is what you’re saying is that the tech you have allows you to avoid that risk?

    That’s right. Let’s start with how do the current systems work? It works using inferior data, frankly. It’s more limited data. It’s outdated. Sometimes it’s inaccurate. And it ignores things like someone’s cash flows, the stability of your income, your savings rate, how money moves through your accounts, or how you use alternative forms of credit—like buy now, pay later, which we have in our ecosystem through Afterpay.

    We have a lot of these signals for our 57 million monthly actives on the Cash App side and for the 4 million small businesses on the Square side, and those, frankly, billions of transaction data points that we have on any given day paired with new technologies. And we intend to continue to be on the forefront of AI, machine learning, and data science to be able to empower more people into the economy. The combination of the superior data and the technologies is what we believe ultimately helps expand access.

    You have a financial background, but not in the financial services industry. Before Block, you were a video game developer at Activision. Are financial businesses and video games similar? Are there things that are similar about them?

    There are. There actually are some things that are similar, I will say. There are many things that are unique to each industry. Each industry is incredibly complex. You find that when big technology companies try to do gaming. They’ve taken over the world in many different ways, but they can’t always crack the nut on putting out a great game. Similarly, some of the largest technology companies have dabbled in fintech but haven’t been able to go as deep, so they’re both very nuanced and complex industries.

    I would say another similarity is that design really matters. Industrial design, the design of products, the interface of products, is absolutely mission-critical to a great game, and it’s absolutely mission-critical to the simplicity and accessibility of our products, be it on Square or Cash App.

    And then maybe the third thing that I would say is that when I was in gaming, at least the business models were rapidly changing from an intermediary distribution mechanism, like releasing a game once and then selling it through a retailer, to an always-on, direct-to-consumer connection. And similarly with banking, people don’t want to bank from 9 to 5, six days a week. They want 24/7 access to their money and the ability to, again, grow their financial livelihood, move their money around seamlessly. So, some similarities are there in that shift to an intermediary model or a slower model to an always-on, direct-to-consumer connection.

    Part of your target audience or your target customer base at Block are Gen Z folks. Did you learn things at Activision about Gen Z that has been useful? Are there things that businesses misunderstand about younger generations still?

    What we’ve learned is that Gen Z, millennial customers, aren’t going to do things the way their parents did. Some of our stats show that 63% of Gen Z customers have moved away from traditional credit cards, and over 80% are skeptical of them. Which means they’re not using a credit card to manage expenses; they’re using a debit card, but then layering on on a transaction-by-transaction basis. Or again, using tools like buy now, pay later, or Cash App Borrow, the means in which they’re managing their consistent cash flows. So that’s an example of how things are changing, and you’ve got to get up to speed with how the next generation of customers expects to manage their money.
    #blocks #cfo #explains #gen #surprising
    Block’s CFO explains Gen Z’s surprising approach to money management
    One stock recently impacted by a whirlwind of volatility is Block—the fintech powerhouse behind Square, Cash App, Tidal Music, and more. The company’s COO and CFO, Amrita Ahuja, shares how her team is using new AI tools to find opportunity amid disruption and reach customers left behind by traditional financial systems. Ahuja also shares lessons from the video game industry and discusses Gen Z’s surprising approach to money management.   This is an abridged transcript of an interview from Rapid Response, hosted by Robert Safian, former editor-in-chief of Fast Company. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. As a leader, when you’re looking at all of this volatility—the tariffs, consumer sentiment’s been unclear, the stock market’s been all over the place. You guys had a huge one-day drop in early May, and it quickly bounced back. How do you make sense of all these external factors? Yeah, our focus is on what we can control. And ultimately, the thing that we are laser-focused on for our business is product velocity. How quickly can we start small with something, launch something for our customers, and then test and iterate and learn so that ultimately, that something that we’ve launched scales into an important product? I’ll give you an example. Cash App Borrow, which is a product where our customers can get access to a line of credit, often that bridges them from paycheck to paycheck. We know so many Americans are living paycheck to paycheck. That’s a product that we launched about three years ago and have now scaled to serve 9 million actives with billion in credit supply to our customers in a span of a couple short years. The more we can be out testing and launching product at a pace, the more we know we are ultimately delivering value to our customers, and the right things will happen from a stock perspective. Block is a financial services provider. You have Square, the point-of-sale system; the digital wallet Cash App, which you mentioned, which competes with Venmo and Robinhood; and a bunch of others. Then you’ve got the buy-now, pay-later leader Afterpay. You chair Square Financial Services, which is Block’s chartered bank. But you’ve said that in the fintech world, Block is only a little bit fin—that comparatively, it’s more tech. Can you explain what you mean by that? What we think is unique about us is our ability as a technology company to completely change innovation in the space, such that we can help solve systemic issues across credit, payments, commerce, and banking. What that means ultimately is we use technologies like AI and machine learning and data science, and we use these technologies in a unique way, in a way that’s different from a traditional bank. We are able to underwrite those who are often frankly forgotten by the traditional financial ecosystems. Our Square Loans product has almost triple the rate of women-owned businesses that we underwrite. Fifty-eight percent of our loans go to women-owned businesses versus 20% for the industry average. For that Cash App Borrow product I was talking about, 70% of those actives, the 9 million actives that we underwrote, fell below 580 as a FICO score. That’s considered a poor FICO score, and yet 97% of repayments are made on time. And this is because we have unique access to data and these technology and tools which can help us uniquely underwrite this often forgotten customer base. Yeah. I mean, credit—sometimes it’s been blamed for financial excesses. But access to credit is also, as you say, an advantage that’s not available to everyone. Do you have a philosophy between those poles—between risk and opportunity? Or is what you’re saying is that the tech you have allows you to avoid that risk? That’s right. Let’s start with how do the current systems work? It works using inferior data, frankly. It’s more limited data. It’s outdated. Sometimes it’s inaccurate. And it ignores things like someone’s cash flows, the stability of your income, your savings rate, how money moves through your accounts, or how you use alternative forms of credit—like buy now, pay later, which we have in our ecosystem through Afterpay. We have a lot of these signals for our 57 million monthly actives on the Cash App side and for the 4 million small businesses on the Square side, and those, frankly, billions of transaction data points that we have on any given day paired with new technologies. And we intend to continue to be on the forefront of AI, machine learning, and data science to be able to empower more people into the economy. The combination of the superior data and the technologies is what we believe ultimately helps expand access. You have a financial background, but not in the financial services industry. Before Block, you were a video game developer at Activision. Are financial businesses and video games similar? Are there things that are similar about them? There are. There actually are some things that are similar, I will say. There are many things that are unique to each industry. Each industry is incredibly complex. You find that when big technology companies try to do gaming. They’ve taken over the world in many different ways, but they can’t always crack the nut on putting out a great game. Similarly, some of the largest technology companies have dabbled in fintech but haven’t been able to go as deep, so they’re both very nuanced and complex industries. I would say another similarity is that design really matters. Industrial design, the design of products, the interface of products, is absolutely mission-critical to a great game, and it’s absolutely mission-critical to the simplicity and accessibility of our products, be it on Square or Cash App. And then maybe the third thing that I would say is that when I was in gaming, at least the business models were rapidly changing from an intermediary distribution mechanism, like releasing a game once and then selling it through a retailer, to an always-on, direct-to-consumer connection. And similarly with banking, people don’t want to bank from 9 to 5, six days a week. They want 24/7 access to their money and the ability to, again, grow their financial livelihood, move their money around seamlessly. So, some similarities are there in that shift to an intermediary model or a slower model to an always-on, direct-to-consumer connection. Part of your target audience or your target customer base at Block are Gen Z folks. Did you learn things at Activision about Gen Z that has been useful? Are there things that businesses misunderstand about younger generations still? What we’ve learned is that Gen Z, millennial customers, aren’t going to do things the way their parents did. Some of our stats show that 63% of Gen Z customers have moved away from traditional credit cards, and over 80% are skeptical of them. Which means they’re not using a credit card to manage expenses; they’re using a debit card, but then layering on on a transaction-by-transaction basis. Or again, using tools like buy now, pay later, or Cash App Borrow, the means in which they’re managing their consistent cash flows. So that’s an example of how things are changing, and you’ve got to get up to speed with how the next generation of customers expects to manage their money. #blocks #cfo #explains #gen #surprising
    WWW.FASTCOMPANY.COM
    Block’s CFO explains Gen Z’s surprising approach to money management
    One stock recently impacted by a whirlwind of volatility is Block—the fintech powerhouse behind Square, Cash App, Tidal Music, and more. The company’s COO and CFO, Amrita Ahuja, shares how her team is using new AI tools to find opportunity amid disruption and reach customers left behind by traditional financial systems. Ahuja also shares lessons from the video game industry and discusses Gen Z’s surprising approach to money management.   This is an abridged transcript of an interview from Rapid Response, hosted by Robert Safian, former editor-in-chief of Fast Company. From the team behind the Masters of Scale podcast, Rapid Response features candid conversations with today’s top business leaders navigating real-time challenges. Subscribe to Rapid Response wherever you get your podcasts to ensure you never miss an episode. As a leader, when you’re looking at all of this volatility—the tariffs, consumer sentiment’s been unclear, the stock market’s been all over the place. You guys had a huge one-day drop in early May, and it quickly bounced back. How do you make sense of all these external factors? Yeah, our focus is on what we can control. And ultimately, the thing that we are laser-focused on for our business is product velocity. How quickly can we start small with something, launch something for our customers, and then test and iterate and learn so that ultimately, that something that we’ve launched scales into an important product? I’ll give you an example. Cash App Borrow, which is a product where our customers can get access to a line of credit, often $100, $200, that bridges them from paycheck to paycheck. We know so many Americans are living paycheck to paycheck. That’s a product that we launched about three years ago and have now scaled to serve 9 million actives with $15 billion in credit supply to our customers in a span of a couple short years. The more we can be out testing and launching product at a pace, the more we know we are ultimately delivering value to our customers, and the right things will happen from a stock perspective. Block is a financial services provider. You have Square, the point-of-sale system; the digital wallet Cash App, which you mentioned, which competes with Venmo and Robinhood; and a bunch of others. Then you’ve got the buy-now, pay-later leader Afterpay. You chair Square Financial Services, which is Block’s chartered bank. But you’ve said that in the fintech world, Block is only a little bit fin—that comparatively, it’s more tech. Can you explain what you mean by that? What we think is unique about us is our ability as a technology company to completely change innovation in the space, such that we can help solve systemic issues across credit, payments, commerce, and banking. What that means ultimately is we use technologies like AI and machine learning and data science, and we use these technologies in a unique way, in a way that’s different from a traditional bank. We are able to underwrite those who are often frankly forgotten by the traditional financial ecosystems. Our Square Loans product has almost triple the rate of women-owned businesses that we underwrite. Fifty-eight percent of our loans go to women-owned businesses versus 20% for the industry average. For that Cash App Borrow product I was talking about, 70% of those actives, the 9 million actives that we underwrote, fell below 580 as a FICO score. That’s considered a poor FICO score, and yet 97% of repayments are made on time. And this is because we have unique access to data and these technology and tools which can help us uniquely underwrite this often forgotten customer base. Yeah. I mean, credit—sometimes it’s been blamed for financial excesses. But access to credit is also, as you say, an advantage that’s not available to everyone. Do you have a philosophy between those poles—between risk and opportunity? Or is what you’re saying is that the tech you have allows you to avoid that risk? That’s right. Let’s start with how do the current systems work? It works using inferior data, frankly. It’s more limited data. It’s outdated. Sometimes it’s inaccurate. And it ignores things like someone’s cash flows, the stability of your income, your savings rate, how money moves through your accounts, or how you use alternative forms of credit—like buy now, pay later, which we have in our ecosystem through Afterpay. We have a lot of these signals for our 57 million monthly actives on the Cash App side and for the 4 million small businesses on the Square side, and those, frankly, billions of transaction data points that we have on any given day paired with new technologies. And we intend to continue to be on the forefront of AI, machine learning, and data science to be able to empower more people into the economy. The combination of the superior data and the technologies is what we believe ultimately helps expand access. You have a financial background, but not in the financial services industry. Before Block, you were a video game developer at Activision. Are financial businesses and video games similar? Are there things that are similar about them? There are. There actually are some things that are similar, I will say. There are many things that are unique to each industry. Each industry is incredibly complex. You find that when big technology companies try to do gaming. They’ve taken over the world in many different ways, but they can’t always crack the nut on putting out a great game. Similarly, some of the largest technology companies have dabbled in fintech but haven’t been able to go as deep, so they’re both very nuanced and complex industries. I would say another similarity is that design really matters. Industrial design, the design of products, the interface of products, is absolutely mission-critical to a great game, and it’s absolutely mission-critical to the simplicity and accessibility of our products, be it on Square or Cash App. And then maybe the third thing that I would say is that when I was in gaming, at least the business models were rapidly changing from an intermediary distribution mechanism, like releasing a game once and then selling it through a retailer, to an always-on, direct-to-consumer connection. And similarly with banking, people don’t want to bank from 9 to 5, six days a week. They want 24/7 access to their money and the ability to, again, grow their financial livelihood, move their money around seamlessly. So, some similarities are there in that shift to an intermediary model or a slower model to an always-on, direct-to-consumer connection. Part of your target audience or your target customer base at Block are Gen Z folks. Did you learn things at Activision about Gen Z that has been useful? Are there things that businesses misunderstand about younger generations still? What we’ve learned is that Gen Z, millennial customers, aren’t going to do things the way their parents did. Some of our stats show that 63% of Gen Z customers have moved away from traditional credit cards, and over 80% are skeptical of them. Which means they’re not using a credit card to manage expenses; they’re using a debit card, but then layering on on a transaction-by-transaction basis. Or again, using tools like buy now, pay later, or Cash App Borrow, the means in which they’re managing their consistent cash flows. So that’s an example of how things are changing, and you’ve got to get up to speed with how the next generation of customers expects to manage their money.
    Like
    Love
    Wow
    Sad
    Angry
    449
    2 Σχόλια 0 Μοιράστηκε
  • How to set up a WhatsApp account without Facebook or Instagram

    There's no shortage of reasons to stay off the Meta ecosystem, which includes Facebook and Instagram, but there are some places where WhatsApp remains the main form of text-based communication. The app is a great alternative to SMS, since it offers end-to-end encryption and was one of the go-to methods to send uncompressed photos and videos between iPhone and Android users before Apple adopted RCS. Even though Facebook, which later rebranded to Meta, acquired WhatsApp in 2014, it doesn't require a Facebook or Instagram account to get on WhatsApp — just a working phone number.
    How to create a WhatsApp account without Facebook or Instagram
    To start, you need to download WhatsApp on your smartphone. Once you open the app, you can start the registration process by entering a working phone number. After entering your phone number, you'll receive a unique six-digit code that will complete the registration process. From there, you can sort through your contacts on your attached smartphone to build out your WhatsApp network, but you won't have to involve Facebook or Instagram at any point.
    Alternatively, you can request a voice call to deliver the code instead. Either way, once you complete the registration process, you have a WhatsApp account that's not tied to a Facebook or Instagram account.
    How to link WhatsApp to other Meta accounts 
    If you change your mind and want more crossover between your Meta apps, you can go into the app's Settings panel to change that. In Settings, you can find the Accounts Center option with the Meta badge on it. Once you hit it, you'll see options to "Add Facebook account" and "Add Instagram account." Linking these accounts means Meta can offer more personalized experiences across the platforms because of the personal data that's now interconnected.
    You can always remove your WhatsApp account from Meta's Account Center by going back into the same Settings panel. However, any previously combined info will stay combined, but Meta will stop combining any personal data after you remove the account.This article originally appeared on Engadget at
    #how #set #whatsapp #account #without
    How to set up a WhatsApp account without Facebook or Instagram
    There's no shortage of reasons to stay off the Meta ecosystem, which includes Facebook and Instagram, but there are some places where WhatsApp remains the main form of text-based communication. The app is a great alternative to SMS, since it offers end-to-end encryption and was one of the go-to methods to send uncompressed photos and videos between iPhone and Android users before Apple adopted RCS. Even though Facebook, which later rebranded to Meta, acquired WhatsApp in 2014, it doesn't require a Facebook or Instagram account to get on WhatsApp — just a working phone number. How to create a WhatsApp account without Facebook or Instagram To start, you need to download WhatsApp on your smartphone. Once you open the app, you can start the registration process by entering a working phone number. After entering your phone number, you'll receive a unique six-digit code that will complete the registration process. From there, you can sort through your contacts on your attached smartphone to build out your WhatsApp network, but you won't have to involve Facebook or Instagram at any point. Alternatively, you can request a voice call to deliver the code instead. Either way, once you complete the registration process, you have a WhatsApp account that's not tied to a Facebook or Instagram account. How to link WhatsApp to other Meta accounts  If you change your mind and want more crossover between your Meta apps, you can go into the app's Settings panel to change that. In Settings, you can find the Accounts Center option with the Meta badge on it. Once you hit it, you'll see options to "Add Facebook account" and "Add Instagram account." Linking these accounts means Meta can offer more personalized experiences across the platforms because of the personal data that's now interconnected. You can always remove your WhatsApp account from Meta's Account Center by going back into the same Settings panel. However, any previously combined info will stay combined, but Meta will stop combining any personal data after you remove the account.This article originally appeared on Engadget at #how #set #whatsapp #account #without
    WWW.ENGADGET.COM
    How to set up a WhatsApp account without Facebook or Instagram
    There's no shortage of reasons to stay off the Meta ecosystem, which includes Facebook and Instagram, but there are some places where WhatsApp remains the main form of text-based communication. The app is a great alternative to SMS, since it offers end-to-end encryption and was one of the go-to methods to send uncompressed photos and videos between iPhone and Android users before Apple adopted RCS. Even though Facebook, which later rebranded to Meta, acquired WhatsApp in 2014, it doesn't require a Facebook or Instagram account to get on WhatsApp — just a working phone number. How to create a WhatsApp account without Facebook or Instagram To start, you need to download WhatsApp on your smartphone. Once you open the app, you can start the registration process by entering a working phone number. After entering your phone number, you'll receive a unique six-digit code that will complete the registration process. From there, you can sort through your contacts on your attached smartphone to build out your WhatsApp network, but you won't have to involve Facebook or Instagram at any point. Alternatively, you can request a voice call to deliver the code instead. Either way, once you complete the registration process, you have a WhatsApp account that's not tied to a Facebook or Instagram account. How to link WhatsApp to other Meta accounts  If you change your mind and want more crossover between your Meta apps, you can go into the app's Settings panel to change that. In Settings, you can find the Accounts Center option with the Meta badge on it. Once you hit it, you'll see options to "Add Facebook account" and "Add Instagram account." Linking these accounts means Meta can offer more personalized experiences across the platforms because of the personal data that's now interconnected. You can always remove your WhatsApp account from Meta's Account Center by going back into the same Settings panel. However, any previously combined info will stay combined, but Meta will stop combining any personal data after you remove the account.This article originally appeared on Engadget at https://www.engadget.com/social-media/how-to-set-up-a-whatsapp-account-without-facebook-or-instagram-210024705.html?src=rss
    Like
    Love
    Wow
    Sad
    Angry
    421
    0 Σχόλια 0 Μοιράστηκε
  • EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs

    Originally published at EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs by Anush Yolyan.

    The integration will deliver simple, accessible, and streamlined email security for vulnerable inboxes

    Global, 4 November 2024 – US-based email security firm EasyDMARC has today announced its integration with Pax8 Marketplace, the leading cloud commerce marketplace. As one of the first DMARC solution providers on the Pax8 Marketplace, EasyDMARC is expanding its mission to protect inboxes from the rising threat of phishing attacks with a rigorous, user-friendly DMARC solution.

    The integration comes as Google highlights the impressive results of recently implemented email authentication measures for bulk senders: a 65% reduction in unauthenticated messages to Gmail users, a 50% increase in bulk senders following best security practices, and 265 billion fewer unauthenticated messages sent in 2024. With email being such a crucial communication channel for businesses, email authentication measures are an essential part of any business’s cybersecurity offering. 

    Key features of the integration include:

    Centralized billing

    With centralized billing, customers can now streamline their cloud services under a single pane of glass, simplifying the management and billing of their EasyDMARC solution. This consolidated approach enables partners to reduce administrative complexity and manage all cloud expenses through one interface, providing a seamless billing and support experience.

    Automated provisioning 

    Through automated provisioning, Pax8’s automation capabilities make deploying DMARC across client accounts quick and hassle-free. By eliminating manual configurations, this integration ensures that customers can implement email security solutions rapidly, allowing them to safeguard client inboxes without delay.

    Bundled offerings

    The bundled offerings available through Pax8 allow partners to enhance their service portfolios by combining EasyDMARC with complementary security solutions. By creating all-in-one security packages, partners can offer their clients more robust protection, addressing a broader range of security needs from a single, trusted platform.

    Gerasim Hovhannisyan, Co-Founder and CEO of EasyDMARC, said:

    “We’re thrilled to be working with Pax8  to provide MSPs with a streamlined, effective way to deliver top-tier email security to their clients, all within a platform that equips them with everything needed to stay secure.  As phishing attacks grow in frequency and sophistication, businesses can no longer afford to overlook the importance of email security. Email authentication is a vital defense against the evolving threat of phishing and is crucial in preserving the integrity of email communication. This integration is designed to allow businesses of all sizes to benefit from DMARC’s extensive capabilities.”

    Ryan Burton, Vice President of Marketplace Vendor Strategy, at Pax8 said: 

    “We’re delighted to welcome EasyDMARC to the Pax8 Marketplace as an enterprise-class DMARC solution provider. This integration gives MSPs the tools they need to meet the growing demand for email security, with simplified deployment, billing, and bundling benefits. With EasyDMARC’s technical capabilities and intelligence, MSPs can deliver robust protection against phishing threats without the technical hassle that often holds businesses back.”

    About EasyDMARC

    EasyDMARC is a cloud-native B2B SaaS solution that addresses email security and deliverability problems with just a few clicks. For Managed Service Providers seeking to increase their revenue, EasyDMARC presents an ideal solution. The email authentication platform streamlines domain management, providing capabilities such as organizational control, domain grouping, and access management.

    Additionally, EasyDMARC offers a comprehensive sales and marketing enablement program designed to boost DMARC sales. All of these features are available for MSPs on a scalable platform with a flexible pay-as-you-go pricing model.

    For more information on the EasyDMARC, visit: /

    About Pax8 

    Pax8 is the technology marketplace of the future, linking partners, vendors, and small to midsized businessesthrough AI-powered insights and comprehensive product support. With a global partner ecosystem of over 38,000 managed service providers, Pax8 empowers SMBs worldwide by providing software and services that unlock their growth potential and enhance their security. Committed to innovating cloud commerce at scale, Pax8 drives customer acquisition and solution consumption across its entire ecosystem.

    Find out more: /

    The post EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs appeared first on EasyDMARC.
    #easydmarc #integrates #with #pax8 #marketplace
    EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs
    Originally published at EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs by Anush Yolyan. The integration will deliver simple, accessible, and streamlined email security for vulnerable inboxes Global, 4 November 2024 – US-based email security firm EasyDMARC has today announced its integration with Pax8 Marketplace, the leading cloud commerce marketplace. As one of the first DMARC solution providers on the Pax8 Marketplace, EasyDMARC is expanding its mission to protect inboxes from the rising threat of phishing attacks with a rigorous, user-friendly DMARC solution. The integration comes as Google highlights the impressive results of recently implemented email authentication measures for bulk senders: a 65% reduction in unauthenticated messages to Gmail users, a 50% increase in bulk senders following best security practices, and 265 billion fewer unauthenticated messages sent in 2024. With email being such a crucial communication channel for businesses, email authentication measures are an essential part of any business’s cybersecurity offering.  Key features of the integration include: Centralized billing With centralized billing, customers can now streamline their cloud services under a single pane of glass, simplifying the management and billing of their EasyDMARC solution. This consolidated approach enables partners to reduce administrative complexity and manage all cloud expenses through one interface, providing a seamless billing and support experience. Automated provisioning  Through automated provisioning, Pax8’s automation capabilities make deploying DMARC across client accounts quick and hassle-free. By eliminating manual configurations, this integration ensures that customers can implement email security solutions rapidly, allowing them to safeguard client inboxes without delay. Bundled offerings The bundled offerings available through Pax8 allow partners to enhance their service portfolios by combining EasyDMARC with complementary security solutions. By creating all-in-one security packages, partners can offer their clients more robust protection, addressing a broader range of security needs from a single, trusted platform. Gerasim Hovhannisyan, Co-Founder and CEO of EasyDMARC, said: “We’re thrilled to be working with Pax8  to provide MSPs with a streamlined, effective way to deliver top-tier email security to their clients, all within a platform that equips them with everything needed to stay secure.  As phishing attacks grow in frequency and sophistication, businesses can no longer afford to overlook the importance of email security. Email authentication is a vital defense against the evolving threat of phishing and is crucial in preserving the integrity of email communication. This integration is designed to allow businesses of all sizes to benefit from DMARC’s extensive capabilities.” Ryan Burton, Vice President of Marketplace Vendor Strategy, at Pax8 said:  “We’re delighted to welcome EasyDMARC to the Pax8 Marketplace as an enterprise-class DMARC solution provider. This integration gives MSPs the tools they need to meet the growing demand for email security, with simplified deployment, billing, and bundling benefits. With EasyDMARC’s technical capabilities and intelligence, MSPs can deliver robust protection against phishing threats without the technical hassle that often holds businesses back.” About EasyDMARC EasyDMARC is a cloud-native B2B SaaS solution that addresses email security and deliverability problems with just a few clicks. For Managed Service Providers seeking to increase their revenue, EasyDMARC presents an ideal solution. The email authentication platform streamlines domain management, providing capabilities such as organizational control, domain grouping, and access management. Additionally, EasyDMARC offers a comprehensive sales and marketing enablement program designed to boost DMARC sales. All of these features are available for MSPs on a scalable platform with a flexible pay-as-you-go pricing model. For more information on the EasyDMARC, visit: / About Pax8  Pax8 is the technology marketplace of the future, linking partners, vendors, and small to midsized businessesthrough AI-powered insights and comprehensive product support. With a global partner ecosystem of over 38,000 managed service providers, Pax8 empowers SMBs worldwide by providing software and services that unlock their growth potential and enhance their security. Committed to innovating cloud commerce at scale, Pax8 drives customer acquisition and solution consumption across its entire ecosystem. Find out more: / The post EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs appeared first on EasyDMARC. #easydmarc #integrates #with #pax8 #marketplace
    EASYDMARC.COM
    EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs
    Originally published at EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs by Anush Yolyan. The integration will deliver simple, accessible, and streamlined email security for vulnerable inboxes Global, 4 November 2024 – US-based email security firm EasyDMARC has today announced its integration with Pax8 Marketplace, the leading cloud commerce marketplace. As one of the first DMARC solution providers on the Pax8 Marketplace, EasyDMARC is expanding its mission to protect inboxes from the rising threat of phishing attacks with a rigorous, user-friendly DMARC solution. The integration comes as Google highlights the impressive results of recently implemented email authentication measures for bulk senders: a 65% reduction in unauthenticated messages to Gmail users, a 50% increase in bulk senders following best security practices, and 265 billion fewer unauthenticated messages sent in 2024. With email being such a crucial communication channel for businesses, email authentication measures are an essential part of any business’s cybersecurity offering.  Key features of the integration include: Centralized billing With centralized billing, customers can now streamline their cloud services under a single pane of glass, simplifying the management and billing of their EasyDMARC solution. This consolidated approach enables partners to reduce administrative complexity and manage all cloud expenses through one interface, providing a seamless billing and support experience. Automated provisioning  Through automated provisioning, Pax8’s automation capabilities make deploying DMARC across client accounts quick and hassle-free. By eliminating manual configurations, this integration ensures that customers can implement email security solutions rapidly, allowing them to safeguard client inboxes without delay. Bundled offerings The bundled offerings available through Pax8 allow partners to enhance their service portfolios by combining EasyDMARC with complementary security solutions. By creating all-in-one security packages, partners can offer their clients more robust protection, addressing a broader range of security needs from a single, trusted platform. Gerasim Hovhannisyan, Co-Founder and CEO of EasyDMARC, said: “We’re thrilled to be working with Pax8  to provide MSPs with a streamlined, effective way to deliver top-tier email security to their clients, all within a platform that equips them with everything needed to stay secure.  As phishing attacks grow in frequency and sophistication, businesses can no longer afford to overlook the importance of email security. Email authentication is a vital defense against the evolving threat of phishing and is crucial in preserving the integrity of email communication. This integration is designed to allow businesses of all sizes to benefit from DMARC’s extensive capabilities.” Ryan Burton, Vice President of Marketplace Vendor Strategy, at Pax8 said:  “We’re delighted to welcome EasyDMARC to the Pax8 Marketplace as an enterprise-class DMARC solution provider. This integration gives MSPs the tools they need to meet the growing demand for email security, with simplified deployment, billing, and bundling benefits. With EasyDMARC’s technical capabilities and intelligence, MSPs can deliver robust protection against phishing threats without the technical hassle that often holds businesses back.” About EasyDMARC EasyDMARC is a cloud-native B2B SaaS solution that addresses email security and deliverability problems with just a few clicks. For Managed Service Providers seeking to increase their revenue, EasyDMARC presents an ideal solution. The email authentication platform streamlines domain management, providing capabilities such as organizational control, domain grouping, and access management. Additionally, EasyDMARC offers a comprehensive sales and marketing enablement program designed to boost DMARC sales. All of these features are available for MSPs on a scalable platform with a flexible pay-as-you-go pricing model. For more information on the EasyDMARC, visit: https://easydmarc.com/ About Pax8  Pax8 is the technology marketplace of the future, linking partners, vendors, and small to midsized businesses (SMBs) through AI-powered insights and comprehensive product support. With a global partner ecosystem of over 38,000 managed service providers, Pax8 empowers SMBs worldwide by providing software and services that unlock their growth potential and enhance their security. Committed to innovating cloud commerce at scale, Pax8 drives customer acquisition and solution consumption across its entire ecosystem. Find out more: https://www.pax8.com/en-us/ The post EasyDMARC Integrates With Pax8 Marketplace To Simplify Email Security For MSPs appeared first on EasyDMARC.
    0 Σχόλια 0 Μοιράστηκε
  • 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
    WWW.MICROSOFT.COM
    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]
    0 Σχόλια 0 Μοιράστηκε
  • Microsoft trolls Apple's new Liquid Glass UI for looking like Windows Vista

    In a nutshell: The OS updates coming to Apple devices later this year will institute the company's first major UI design shift in over a decade, but eagle-eyed observers noticed similarities with an old version of Windows – comparisons that haven't escaped Microsoft's notice. Thankfully, users concerned about Apple's upcoming interface will have options to change its visual presentation.
    Some of Microsoft's social media accounts recently poked fun at the upcoming "Liquid Glass" user interface design language Apple unveiled at WWDC this week. Although the Cupertino giant has hailed the update as a major innovation, many immediately began comparing it to Microsoft's nearly two-decade-old Windows Vista UI.

     

     
     

     

    View this post on Instagram

     

     
     
     

     
     

     
     
     

     
     

    A post shared by WindowsLiquid Glass is Apple's name for the new visual style arriving in iOS 26, iPadOS 26, macOS 26 Tahoe, watchOS 26, and tvOS 26, which will launch this fall. Inspired by the Apple Vision Pro's visionOS, the design language favors rounded edges and transparent backgrounds for inputs and other UI functions.
    It is Apple's most significant design change since iOS 7 debuted almost 12 years ago, and the first to establish a unified language across all of the company's devices.
    On the left: nice Liquid Glass UI minimalistic look. On the right: Liquid Glass looking all kinds of wrong in the current beta.

    Apps, wallpapers, and other background content will be visible through app icons, notifications, and menu elements for a glass-like appearance. Apple claims that the effect will improve cohesion across the interface, but beta testers are concerned that text will become less readable.
    Others, including Microsoft, mocked the update's resemblance to Windows Vista's glass-like "Aero" aesthetic, which debuted in 2007. That OS also made UI elements partially transparent, but Microsoft eventually phased it out when it began moving toward its current design language.
    The official Windows Instagram account recently responded to Apple's presentation by posting a slideshow of Vista screenshots played over a nostalgic Windows boot tune. The Windows Twitter account also shared a picture recalling the Vista-era profile icons.
    Other social media users joined in on the fun. Some highlighted the unfortunate placement of the YouTube icon in Apple's Liquid Glass explainer video, which the company altered. Others compared the design language to the unique chassis for Apple's 2000 Power Mac G4 Cube and the main menu for Nintendo's 2012 Wii U game console.
    Fortunately, users can customize Liquid Glass by switching between transparent, light, and dark modes. They can also opt for a slightly more opaque presentation with a toggle located under Settings > Accessibility > Display & Text Size > Reduce Transparency.
    #microsoft #trolls #apple039s #new #liquid
    Microsoft trolls Apple's new Liquid Glass UI for looking like Windows Vista
    In a nutshell: The OS updates coming to Apple devices later this year will institute the company's first major UI design shift in over a decade, but eagle-eyed observers noticed similarities with an old version of Windows – comparisons that haven't escaped Microsoft's notice. Thankfully, users concerned about Apple's upcoming interface will have options to change its visual presentation. Some of Microsoft's social media accounts recently poked fun at the upcoming "Liquid Glass" user interface design language Apple unveiled at WWDC this week. Although the Cupertino giant has hailed the update as a major innovation, many immediately began comparing it to Microsoft's nearly two-decade-old Windows Vista UI.         View this post on Instagram                       A post shared by WindowsLiquid Glass is Apple's name for the new visual style arriving in iOS 26, iPadOS 26, macOS 26 Tahoe, watchOS 26, and tvOS 26, which will launch this fall. Inspired by the Apple Vision Pro's visionOS, the design language favors rounded edges and transparent backgrounds for inputs and other UI functions. It is Apple's most significant design change since iOS 7 debuted almost 12 years ago, and the first to establish a unified language across all of the company's devices. On the left: nice Liquid Glass UI minimalistic look. On the right: Liquid Glass looking all kinds of wrong in the current beta. Apps, wallpapers, and other background content will be visible through app icons, notifications, and menu elements for a glass-like appearance. Apple claims that the effect will improve cohesion across the interface, but beta testers are concerned that text will become less readable. Others, including Microsoft, mocked the update's resemblance to Windows Vista's glass-like "Aero" aesthetic, which debuted in 2007. That OS also made UI elements partially transparent, but Microsoft eventually phased it out when it began moving toward its current design language. The official Windows Instagram account recently responded to Apple's presentation by posting a slideshow of Vista screenshots played over a nostalgic Windows boot tune. The Windows Twitter account also shared a picture recalling the Vista-era profile icons. Other social media users joined in on the fun. Some highlighted the unfortunate placement of the YouTube icon in Apple's Liquid Glass explainer video, which the company altered. Others compared the design language to the unique chassis for Apple's 2000 Power Mac G4 Cube and the main menu for Nintendo's 2012 Wii U game console. Fortunately, users can customize Liquid Glass by switching between transparent, light, and dark modes. They can also opt for a slightly more opaque presentation with a toggle located under Settings > Accessibility > Display & Text Size > Reduce Transparency. #microsoft #trolls #apple039s #new #liquid
    WWW.TECHSPOT.COM
    Microsoft trolls Apple's new Liquid Glass UI for looking like Windows Vista
    In a nutshell: The OS updates coming to Apple devices later this year will institute the company's first major UI design shift in over a decade, but eagle-eyed observers noticed similarities with an old version of Windows – comparisons that haven't escaped Microsoft's notice. Thankfully, users concerned about Apple's upcoming interface will have options to change its visual presentation. Some of Microsoft's social media accounts recently poked fun at the upcoming "Liquid Glass" user interface design language Apple unveiled at WWDC this week. Although the Cupertino giant has hailed the update as a major innovation, many immediately began comparing it to Microsoft's nearly two-decade-old Windows Vista UI.         View this post on Instagram                       A post shared by Windows (@windows) Liquid Glass is Apple's name for the new visual style arriving in iOS 26, iPadOS 26, macOS 26 Tahoe, watchOS 26, and tvOS 26, which will launch this fall. Inspired by the Apple Vision Pro's visionOS, the design language favors rounded edges and transparent backgrounds for inputs and other UI functions. It is Apple's most significant design change since iOS 7 debuted almost 12 years ago, and the first to establish a unified language across all of the company's devices. On the left: nice Liquid Glass UI minimalistic look. On the right: Liquid Glass looking all kinds of wrong in the current beta. Apps, wallpapers, and other background content will be visible through app icons, notifications, and menu elements for a glass-like appearance. Apple claims that the effect will improve cohesion across the interface, but beta testers are concerned that text will become less readable. Others, including Microsoft, mocked the update's resemblance to Windows Vista's glass-like "Aero" aesthetic, which debuted in 2007. That OS also made UI elements partially transparent, but Microsoft eventually phased it out when it began moving toward its current design language. The official Windows Instagram account recently responded to Apple's presentation by posting a slideshow of Vista screenshots played over a nostalgic Windows boot tune. The Windows Twitter account also shared a picture recalling the Vista-era profile icons. Other social media users joined in on the fun. Some highlighted the unfortunate placement of the YouTube icon in Apple's Liquid Glass explainer video, which the company altered. Others compared the design language to the unique chassis for Apple's 2000 Power Mac G4 Cube and the main menu for Nintendo's 2012 Wii U game console. Fortunately, users can customize Liquid Glass by switching between transparent, light, and dark modes. They can also opt for a slightly more opaque presentation with a toggle located under Settings > Accessibility > Display & Text Size > Reduce Transparency.
    0 Σχόλια 0 Μοιράστηκε