The new Street Fighter movie has added a few more names to the cast. Earlier this week, rapper-turned-actor Curtis "50 Cent" Jackson teased that he will play Balrog in the film. That rumor now appears to be confirmed, and another performer has signed up to play Dan Hibiki, one of the weakest fighters in the Street Fighter universe.According to Deadline, comedian Andrew Schulz will play Dan in the film. That's appropriate, since Dan is largely a comic relief character who gets played for laughs. This will mark Dan's first-ever appearance in live-action media. Schulz has previously appeared in The Underdoggs and the remake of White Men Can’t Jump, as well as the second season of Netflix's sitcom Tires. He is also the host of Flagrant Pod, a popular comedy podcast, and his most recent comedy special was streamed on Netflix.Jackson's casting as Balrog was confirmed in a subsequent report by The Hollywood Reporter. THR went a step further by confirming the roles of a few previously cast actors including Andrew Koji as Ryu, Noah Centineo as Ken, Jason Momoa as Blanka, and Orville Peck as Vega. Additionally, the outlet notes that Joe "Roman Reigns" Anoa’i, a longtime WWE superstar and former World Champion, will play Akuma, one of the film's primary villains.Continue Reading at GameSpot

By TREVOR HOGG Images courtesy of Warner Bros. Pictures. Rather than a world constructed around photorealistic pixels, a video game created by Markus Persson has taken the boxier 3D voxel route, which has become its signature aesthetic, and sparked an international phenomenon that finally gets adapted into a feature with the release of A Minecraft Movie. Brought onboard to help filmmaker Jared Hess in creating the environments that the cast of Jason Momoa, Jack Black, Sebastian Hansen, Emma Myers and Danielle Brooks find themselves inhabiting was Disguise under the direction of Production VFX Supervisor Dan Lemmon. “[A]s the Senior Unreal Artist within the Virtual Art Department (VAD) on Minecraft, I experienced the full creative workflow. What stood out most was how deeply the VAD was embedded across every stage of production. We weren’t working in isolation. From the production designer and director to the VFX supervisor and DP, the VAD became a hub for collaboration.” —Talia Finlayson, Creative Technologist, Disguise Interior and exterior environments had to be created, such as the shop owned by Steve (Jack Black). “Prior to working on A Minecraft Movie, I held more technical roles, like serving as the Virtual Production LED Volume Operator on a project for Apple TV+ and Paramount Pictures,” notes Talia Finlayson, Creative Technologist for Disguise. “But as the Senior Unreal Artist within the Virtual Art Department (VAD) on Minecraft, I experienced the full creative workflow. What stood out most was how deeply the VAD was embedded across every stage of production. We weren’t working in isolation. From the production designer and director to the VFX supervisor and DP, the VAD became a hub for collaboration.” The project provided new opportunities. “I’ve always loved the physicality of working with an LED volume, both for the immersion it provides and the way that seeing the environment helps shape an actor’s performance,” notes Laura Bell, Creative Technologist for Disguise. “But for A Minecraft Movie, we used Simulcam instead, and it was an incredible experience to live-composite an entire Minecraft world in real-time, especially with nothing on set but blue curtains.” Set designs originally created by the art department in Rhinoceros 3D were transformed into fully navigable 3D environments within Unreal Engine. “These scenes were far more than visualizations,” Finlayson remarks. “They were interactive tools used throughout the production pipeline. We would ingest 3D models and concept art, clean and optimize geometry using tools like Blender, Cinema 4D or Maya, then build out the world in Unreal Engine. This included applying materials, lighting and extending environments. These Unreal scenes we created were vital tools across the production and were used for a variety of purposes such as enabling the director to explore shot compositions, block scenes and experiment with camera movement in a virtual space, as well as passing along Unreal Engine scenes to the visual effects vendors so they could align their digital environments and set extensions with the approved production layouts.” A virtual exploration of Steve’s shop in Midport Village. Certain elements have to be kept in mind when constructing virtual environments. “When building virtual environments, you need to consider what can actually be built, how actors and cameras will move through the space, and what’s safe and practical on set,” Bell observes. “Outside the areas where strict accuracy is required, you want the environments to blend naturally with the original designs from the art department and support the story, creating a space that feels right for the scene, guides the audience’s eye and sets the right tone. Things like composition, lighting and small environmental details can be really fun to work on, but also serve as beautiful additions to help enrich a story.” “I’ve always loved the physicality of working with an LED volume, both for the immersion it provides and the way that seeing the environment helps shape an actor’s performance. But for A Minecraft Movie, we used Simulcam instead, and it was an incredible experience to live-composite an entire Minecraft world in real-time, especially with nothing on set but blue curtains.” —Laura Bell, Creative Technologist, Disguise Among the buildings that had to be created for Midport Village was Steve’s (Jack Black) Lava Chicken Shack. Concept art was provided that served as visual touchstones. “We received concept art provided by the amazing team of concept artists,” Finlayson states. “Not only did they send us 2D artwork, but they often shared the 3D models they used to create those visuals. These models were incredibly helpful as starting points when building out the virtual environments in Unreal Engine; they gave us a clear sense of composition and design intent. Storyboards were also a key part of the process and were constantly being updated as the project evolved. Having access to the latest versions allowed us to tailor the virtual environments to match camera angles, story beats and staging. Sometimes we would also help the storyboard artists by sending through images of the Unreal Engine worlds to help them geographically position themselves in the worlds and aid in their storyboarding.” At times, the video game assets came in handy. “Exteriors often involved large-scale landscapes and stylized architectural elements, which had to feel true to the Minecraft world,” Finlayson explains. “In some cases, we brought in geometry from the game itself to help quickly block out areas. For example, we did this for the Elytra Flight Chase sequence, which takes place through a large canyon.” Flexibility was critical. “A key technical challenge we faced was ensuring that the Unreal levels were built in a way that allowed for fast and flexible iteration,” Finlayson remarks. “Since our environments were constantly being reviewed by the director, production designer, DP and VFX supervisor, we needed to be able to respond quickly to feedback, sometimes live during a review session. To support this, we had to keep our scenes modular and well-organized; that meant breaking environments down into manageable components and maintaining clean naming conventions. By setting up the levels this way, we could make layout changes, swap assets or adjust lighting on the fly without breaking the scene or slowing down the process.” Production schedules influence the workflows, pipelines and techniques. “No two projects will ever feel exactly the same,” Bell notes. “For example, Pat Younis [VAD Art Director] adapted his typical VR setup to allow scene reviews using a PS5 controller, which made it much more comfortable and accessible for the director. On a more technical side, because everything was cubes and voxels, my Blender workflow ended up being way heavier on the re-mesh modifier than usual, definitely not something I’ll run into again anytime soon!” A virtual study and final still of the cast members standing outside of the Lava Chicken Shack. “We received concept art provided by the amazing team of concept artists. Not only did they send us 2D artwork, but they often shared the 3D models they used to create those visuals. These models were incredibly helpful as starting points when building out the virtual environments in Unreal Engine; they gave us a clear sense of composition and design intent. Storyboards were also a key part of the process and were constantly being updated as the project evolved. Having access to the latest versions allowed us to tailor the virtual environments to match camera angles, story beats and staging.” —Talia Finlayson, Creative Technologist, Disguise The design and composition of virtual environments tended to remain consistent throughout principal photography. “The only major design change I can recall was the removal of a second story from a building in Midport Village to allow the camera crane to get a clear shot of the chicken perched above Steve’s lava chicken shack,” Finlayson remarks. “I would agree that Midport Village likely went through the most iterations,” Bell responds. “The archway, in particular, became a visual anchor across different levels. We often placed it off in the distance to help orient both ourselves and the audience and show how far the characters had traveled. I remember rebuilding the stairs leading up to the rampart five or six times, using different configurations based on the physically constructed stairs. This was because there were storyboarded sequences of the film’s characters, Henry, Steve and Garrett, being chased by piglins, and the action needed to match what could be achieved practically on set.” Virtually conceptualizing the layout of Midport Village. Complex virtual environments were constructed for the final battle and the various forest scenes throughout the movie. “What made these particularly challenging was the way physical set pieces were repurposed and repositioned to serve multiple scenes and locations within the story,” Finlayson reveals. “The same built elements had to appear in different parts of the world, so we had to carefully adjust the virtual environments to accommodate those different positions.” Bell is in agreement with her colleague. “The forest scenes were some of the more complex environments to manage. It could get tricky, particularly when the filming schedule shifted. There was one day on set where the order of shots changed unexpectedly, and because the physical sets looked so similar, I initially loaded a different perspective than planned. Fortunately, thanks to our workflow, Lindsay George [VP Tech] and I were able to quickly open the recorded sequence in Unreal Engine and swap out the correct virtual environment for the live composite without any disruption to the shoot.” An example of the virtual and final version of the Woodland Mansion. “Midport Village likely went through the most iterations. The archway, in particular, became a visual anchor across different levels. We often placed it off in the distance to help orient both ourselves and the audience and show how far the characters had traveled.” —Laura Bell, Creative Technologist, Disguise Extensive detail was given to the center of the sets where the main action unfolds. “For these areas, we received prop layouts from the prop department to ensure accurate placement and alignment with the physical builds,” Finlayson explains. “These central environments were used heavily for storyboarding, blocking and department reviews, so precision was essential. As we moved further out from the practical set, the environments became more about blocking and spatial context rather than fine detail. We worked closely with Production Designer Grant Major to get approval on these extended environments, making sure they aligned with the overall visual direction. We also used creatures and crowd stand-ins provided by the visual effects team. These gave a great sense of scale and placement during early planning stages and allowed other departments to better understand how these elements would be integrated into the scenes.” Cast members Sebastian Hansen, Danielle Brooks and Emma Myers stand in front of the Earth Portal Plateau environment. Doing a virtual scale study of the Mountainside. Practical requirements like camera moves, stunt choreography and crane setups had an impact on the creation of virtual environments. “Sometimes we would adjust layouts slightly to open up areas for tracking shots or rework spaces to accommodate key action beats, all while keeping the environment feeling cohesive and true to the Minecraft world,” Bell states. “Simulcam bridged the physical and virtual worlds on set, overlaying Unreal Engine environments onto live-action scenes in real-time, giving the director, DP and other department heads a fully-realized preview of shots and enabling precise, informed decisions during production. It also recorded critical production data like camera movement paths, which was handed over to the post-production team to give them the exact tracks they needed, streamlining the visual effects pipeline.” Piglots cause mayhem during the Wingsuit Chase. Virtual versions of the exterior and interior of the Safe House located in the Enchanted Woods. “One of the biggest challenges for me was managing constant iteration while keeping our environments clean, organized and easy to update,” Finlayson notes. “Because the virtual sets were reviewed regularly by the director and other heads of departments, feedback was often implemented live in the room. This meant the environments had to be flexible. But overall, this was an amazing project to work on, and I am so grateful for the incredible VAD team I was a part of – Heide Nichols [VAD Supervisor], Pat Younis, Jake Tuck [Unreal Artist] and Laura. Everyone on this team worked so collaboratively, seamlessly and in such a supportive way that I never felt like I was out of my depth.” There was another challenge that is more to do with familiarity. “Having a VAD on a film is still a relatively new process in production,” Bell states. “There were moments where other departments were still learning what we did and how to best work with us. That said, the response was overwhelmingly positive. I remember being on set at the Simulcam station and seeing how excited people were to look at the virtual environments as they walked by, often stopping for a chat and a virtual tour. Instead of seeing just a huge blue curtain, they were stoked to see something Minecraft and could get a better sense of what they were actually shooting.”

Here's an overview of the most recent Blender jobs on Blender Artists, ArtStation and 3djobs.xyz: Looking for someone to create some low poly cartoonish clothing asset for my character I'm looking for an artist to make me a Thumbnail for YouTube Vert

Reading Time: 9 minutes In marketing, data isn’t a buzzword. It’s the lifeblood of all successful campaigns. But are you truly harnessing its power, or are you drowning in a sea of information? To answer this question (and many others), we sat down with Ankur Kothari, a seasoned Martech expert, to dive deep into this crucial topic. This interview, originally conducted for Chapter 6 of “The Customer Engagement Book: Adapt or Die” explores how businesses can translate raw data into actionable insights that drive real results. Ankur shares his wealth of knowledge on identifying valuable customer engagement data, distinguishing between signal and noise, and ultimately, shaping real-time strategies that keep companies ahead of the curve.   Ankur Kothari Q&A Interview 1. What types of customer engagement data are most valuable for making strategic business decisions? Primarily, there are four different buckets of customer engagement data. I would begin with behavioral data, encompassing website interaction, purchase history, and other app usage patterns. Second would be demographic information: age, location, income, and other relevant personal characteristics. Third would be sentiment analysis, where we derive information from social media interaction, customer feedback, or other customer reviews. Fourth would be the customer journey data. We track touchpoints across various channels of the customers to understand the customer journey path and conversion. Combining these four primary sources helps us understand the engagement data. 2. How do you distinguish between data that is actionable versus data that is just noise? First is keeping relevant to your business objectives, making actionable data that directly relates to your specific goals or KPIs, and then taking help from statistical significance. Actionable data shows clear patterns or trends that are statistically valid, whereas other data consists of random fluctuations or outliers, which may not be what you are interested in. You also want to make sure that there is consistency across sources. Actionable insights are typically corroborated by multiple data points or channels, while other data or noise can be more isolated and contradictory. Actionable data suggests clear opportunities for improvement or decision making, whereas noise does not lead to meaningful actions or changes in strategy. By applying these criteria, I can effectively filter out the noise and focus on data that delivers or drives valuable business decisions. 3. How can customer engagement data be used to identify and prioritize new business opportunities? First, it helps us to uncover unmet needs. By analyzing the customer feedback, touch points, support interactions, or usage patterns, we can identify the gaps in our current offerings or areas where customers are experiencing pain points. Second would be identifying emerging needs. Monitoring changes in customer behavior or preferences over time can reveal new market trends or shifts in demand, allowing my company to adapt their products or services accordingly. Third would be segmentation analysis. Detailed customer data analysis enables us to identify unserved or underserved segments or niche markets that may represent untapped opportunities for growth or expansion into newer areas and new geographies. Last is to build competitive differentiation. Engagement data can highlight where our companies outperform competitors, helping us to prioritize opportunities that leverage existing strengths and unique selling propositions. 4. Can you share an example of where data insights directly influenced a critical decision? I will share an example from my previous organization at one of the financial services where we were very data-driven, which made a major impact on our critical decision regarding our credit card offerings. We analyzed the customer engagement data, and we discovered that a large segment of our millennial customers were underutilizing our traditional credit cards but showed high engagement with mobile payment platforms. That insight led us to develop and launch our first digital credit card product with enhanced mobile features and rewards tailored to the millennial spending habits. Since we had access to a lot of transactional data as well, we were able to build a financial product which met that specific segment’s needs. That data-driven decision resulted in a 40% increase in our new credit card applications from this demographic within the first quarter of the launch. Subsequently, our market share improved in that specific segment, which was very crucial. 5. Are there any other examples of ways that you see customer engagement data being able to shape marketing strategy in real time? When it comes to using the engagement data in real-time, we do quite a few things. In the recent past two, three years, we are using that for dynamic content personalization, adjusting the website content, email messaging, or ad creative based on real-time user behavior and preferences. We automate campaign optimization using specific AI-driven tools to continuously analyze performance metrics and automatically reallocate the budget to top-performing channels or ad segments. Then we also build responsive social media engagement platforms like monitoring social media sentiments and trending topics to quickly adapt the messaging and create timely and relevant content. With one-on-one personalization, we do a lot of A/B testing as part of the overall rapid testing and market elements like subject lines, CTAs, and building various successful variants of the campaigns. 6. How are you doing the 1:1 personalization? We have advanced CDP systems, and we are tracking each customer’s behavior in real-time. So the moment they move to different channels, we know what the context is, what the relevance is, and the recent interaction points, so we can cater the right offer. So for example, if you looked at a certain offer on the website and you came from Google, and then the next day you walk into an in-person interaction, our agent will already know that you were looking at that offer. That gives our customer or potential customer more one-to-one personalization instead of just segment-based or bulk interaction kind of experience. We have a huge team of data scientists, data analysts, and AI model creators who help us to analyze big volumes of data and bring the right insights to our marketing and sales team so that they can provide the right experience to our customers. 7. What role does customer engagement data play in influencing cross-functional decisions, such as with product development, sales, and customer service? Primarily with product development — we have different products, not just the financial products or products whichever organizations sell, but also various products like mobile apps or websites they use for transactions. So that kind of product development gets improved. The engagement data helps our sales and marketing teams create more targeted campaigns, optimize channel selection, and refine messaging to resonate with specific customer segments. Customer service also gets helped by anticipating common issues, personalizing support interactions over the phone or email or chat, and proactively addressing potential problems, leading to improved customer satisfaction and retention. So in general, cross-functional application of engagement improves the customer-centric approach throughout the organization. 8. What do you think some of the main challenges marketers face when trying to translate customer engagement data into actionable business insights? I think the huge amount of data we are dealing with. As we are getting more digitally savvy and most of the customers are moving to digital channels, we are getting a lot of data, and that sheer volume of data can be overwhelming, making it very difficult to identify truly meaningful patterns and insights. Because of the huge data overload, we create data silos in this process, so information often exists in separate systems across different departments. We are not able to build a holistic view of customer engagement. Because of data silos and overload of data, data quality issues appear. There is inconsistency, and inaccurate data can lead to incorrect insights or poor decision-making. Quality issues could also be due to the wrong format of the data, or the data is stale and no longer relevant. As we are growing and adding more people to help us understand customer engagement, I’ve also noticed that technical folks, especially data scientists and data analysts, lack skills to properly interpret the data or apply data insights effectively. So there’s a lack of understanding of marketing and sales as domains. It’s a huge effort and can take a lot of investment. Not being able to calculate the ROI of your overall investment is a big challenge that many organizations are facing. 9. Why do you think the analysts don’t have the business acumen to properly do more than analyze the data? If people do not have the right idea of why we are collecting this data, we collect a lot of noise, and that brings in huge volumes of data. If you cannot stop that from step one—not bringing noise into the data system—that cannot be done by just technical folks or people who do not have business knowledge. Business people do not know everything about what data is being collected from which source and what data they need. It’s a gap between business domain knowledge, specifically marketing and sales needs, and technical folks who don’t have a lot of exposure to that side. Similarly, marketing business people do not have much exposure to the technical side — what’s possible to do with data, how much effort it takes, what’s relevant versus not relevant, and how to prioritize which data sources will be most important. 10. Do you have any suggestions for how this can be overcome, or have you seen it in action where it has been solved before? First, cross-functional training: training different roles to help them understand why we’re doing this and what the business goals are, giving technical people exposure to what marketing and sales teams do. And giving business folks exposure to the technology side through training on different tools, strategies, and the roadmap of data integrations. The second is helping teams work more collaboratively. So it’s not like the technology team works in a silo and comes back when their work is done, and then marketing and sales teams act upon it. Now we’re making it more like one team. You work together so that you can complement each other, and we have a better strategy from day one. 11. How do you address skepticism or resistance from stakeholders when presenting data-driven recommendations? We present clear business cases where we demonstrate how data-driven recommendations can directly align with business objectives and potential ROI. We build compelling visualizations, easy-to-understand charts and graphs that clearly illustrate the insights and the implications for business goals. We also do a lot of POCs and pilot projects with small-scale implementations to showcase tangible results and build confidence in the data-driven approach throughout the organization. 12. What technologies or tools have you found most effective for gathering and analyzing customer engagement data? I’ve found that Customer Data Platforms help us unify customer data from various sources, providing a comprehensive view of customer interactions across touch points. Having advanced analytics platforms — tools with AI and machine learning capabilities that can process large volumes of data and uncover complex patterns and insights — is a great value to us. We always use, or many organizations use, marketing automation systems to improve marketing team productivity, helping us track and analyze customer interactions across multiple channels. Another thing is social media listening tools, wherever your brand is mentioned or you want to measure customer sentiment over social media, or track the engagement of your campaigns across social media platforms. Last is web analytical tools, which provide detailed insights into your website visitors’ behaviors and engagement metrics, for browser apps, small browser apps, various devices, and mobile apps. 13. How do you ensure data quality and consistency across multiple channels to make these informed decisions? We established clear guidelines for data collection, storage, and usage across all channels to maintain consistency. Then we use data integration platforms — tools that consolidate data from various sources into a single unified view, reducing discrepancies and inconsistencies. While we collect data from different sources, we clean the data so it becomes cleaner with every stage of processing. We also conduct regular data audits — performing periodic checks to identify and rectify data quality issues, ensuring accuracy and reliability of information. We also deploy standardized data formats. On top of that, we have various automated data cleansing tools, specific software to detect and correct data errors, redundancies, duplicates, and inconsistencies in data sets automatically. 14. How do you see the role of customer engagement data evolving in shaping business strategies over the next five years? The first thing that’s been the biggest trend from the past two years is AI-driven decision making, which I think will become more prevalent, with advanced algorithms processing vast amounts of engagement data in real-time to inform strategic choices. Somewhat related to this is predictive analytics, which will play an even larger role, enabling businesses to anticipate customer needs and market trends with more accuracy and better predictive capabilities. We also touched upon hyper-personalization. We are all trying to strive toward more hyper-personalization at scale, which is more one-on-one personalization, as we are increasingly capturing more engagement data and have bigger systems and infrastructure to support processing those large volumes of data so we can achieve those hyper-personalization use cases. As the world is collecting more data, privacy concerns and regulations come into play. I believe in the next few years there will be more innovation toward how businesses can collect data ethically and what the usage practices are, leading to more transparent and consent-based engagement data strategies. And lastly, I think about the integration of engagement data, which is always a big challenge. I believe as we’re solving those integration challenges, we are adding more and more complex data sources to the picture. So I think there will need to be more innovation or sophistication brought into data integration strategies, which will help us take a truly customer-centric approach to strategy formulation.   This interview Q&A was hosted with Ankur Kothari, a previous Martech Executive, for Chapter 6 of The Customer Engagement Book: Adapt or Die. Download the PDF or request a physical copy of the book here. The post Ankur Kothari Q&A: Customer Engagement Book Interview appeared first on MoEngage.

Secretly working multiple full-time remote jobs may sound like a nightmare — but Americans looking to make their financial dreams come true willingly hustle for it.Over the past two years, Business Insider has interviewed more than two dozen "overemployed" workers, many of whom work in tech roles. They tend to work long hours but say the extra earnings are worth it to pay off student debt, save for an early retirement, and afford expensive vacations and weight-loss drugs. Many started working multiple jobs during the pandemic, when remote job openings soared.One example is Sarah, who's on track to earn about $300,000 this year by secretly working two remote IT jobs. Over the last few years, Sarah said the extra income from job juggling has helped her save more than $100,000 in her 401(k)s, pay off $17,000 in credit card debt, and furnish her home.Sarah, who's in her 50s and lives in the Southeast, said working 12-hour days is worth it for the job security. This security came in handy when she was laid off from one of her jobs last year. She's since found a new second gig."I want to ride this out until I retire," Sarah previously told BI. Business Insider verified her identity, but she asked to use a pseudonym, citing fears of professional repercussions. BI spoke to one boss who caught an employee secretly working another job and fired him. Job juggling could breach some employment contracts and be a fireable offense.Overemployed workers like Sarah told BI how they've landed extra roles, juggled the workload, and stayed under the radar. Some said they rely on tactics like blocking off calendars, using separate devices, minimizing meetings, and sticking to flexible roles with low oversight. While job juggling could have professional repercussions or lead to burnout, and some readers have questioned the ethics of this working arrangement, many workers have told BI they don't feel guilty about their job juggling — and that the financial benefits generally outweigh the downsides and risks. In recent years, some have struggled to land new remote gigs, due in part to hiring slowdowns and return-to-office mandates. Most said they plan to continue pursuing overemployment as long as they can.Read the stories ahead to learn how some Americans have managed the workload, risks, and stress of working multiple jobs — and transformed their finances.

Key Takeaways Meta and Yandex have been found guilty of secretly listening to localhost ports and using them to transfer sensitive data from Android devices. The corporations use Meta Pixel and Yandex Metrica scripts to transfer cookies from browsers to local apps. Using incognito mode or a VPN can’t fully protect users against it. A Meta spokesperson has called this a ‘miscommunication,’ which seems to be an attempt to underplay the situation. Denmark’s Ministry of Digitalization has recently announced that it will leave the Microsoft ecosystem in favor of Linux and other open-source software. Minister Caroline Stage Olsen revealed this in an interview with Politiken, the country’s leading newspaper. According to Olsen, the Ministry plans to switch half of its employees to Linux and LibreOffice by summer, and the rest by fall. The announcement comes after Denmark’s largest cities – Copenhagen and Aarhus – made similar moves earlier this month. Why the Danish Ministry of Digitalization Switched to Open-Source Software The three main reasons Denmark is moving away from Microsoft are costs, politics, and security. In the case of Aarhus, the city was able to slash its annual costs from 800K kroner to just 225K by replacing Microsoft with a German service provider.  The same is a pain point for Copenhagen, which saw its costs on Microsoft balloon from 313M kroner in 2018 to 538M kroner in 2023. It’s also part of a broader move to increase its digital sovereignty. In her LinkedIn post, Olsen further explained that the strategy is not about isolation or digital nationalism, adding that they should not turn their backs completely on global tech companies like Microsoft.  Instead, it’s about avoiding being too dependent on these companies, which could prevent them from acting freely. Then there’s politics. Since his reelection earlier this year, US President Donald Trump has repeatedly threatened to take over Greenland, an autonomous territory of Denmark.  In May, the Danish Foreign Minister Lars Løkke Rasmussen summoned the US ambassador regarding news that US spy agencies have been told to focus on the territory. If the relationship between the two countries continues to erode, Trump can order Microsoft and other US tech companies to cut off Denmark from their services. After all, Microsoft and Facebook’s parent company Meta, have close ties to the US president after contributing $1M each for his inauguration in January. Denmark Isn’t Alone: Other EU Countries Are Making Similar Moves Denmark is only one of the growing number of European Union (EU) countries taking measures to become more digitally independent. Germany’s Federal Digital Minister Karsten Wildberger emphasized the need to be more independent of global tech companies during the re:publica internet conference in May. He added that IT companies in the EU have the opportunity to create tech that is based on the region’s values. Meanwhile, Bert Hubert, a technical advisor to the Dutch Electoral Council, wrote in February that ‘it is no longer safe to move our governments and societies to US clouds.’ He said that America is no longer a ‘reliable partner,’ making it risky to have the data of European governments and businesses at the mercy of US-based cloud providers. Earlier this month, the chief prosecutor of the International Criminal Court (ICC), Karim Khan, experienced a disconnection from his Microsoft-based email account, sparking uproar across the region.  Speculation quickly arose that the incident was linked to sanctions previously imposed on the ICC by the Trump administration, an assertion Microsoft has denied. Earlier this month, the chief prosecutor of the International Criminal Court (ICC), Karim Khan, disconnection from his Microsoft-based email account caused an uproar in the region. Some speculated that this was connected to sanctions imposed by Trump against the ICC, which Microsoft denied. Weaning the EU Away from US Tech is Possible, But Challenges Lie Ahead Change like this doesn’t happen overnight. Just finding, let alone developing, reliable alternatives to tools that have been part of daily workflows for decades, is a massive undertaking. It will also take time for users to adapt to these new tools, especially when transitioning to an entirely new ecosystem. In Aarhus, for example, municipal staff initially viewed the shift to open source as a step down from the familiarity and functionality of Microsoft products. Overall, these are only temporary hurdles. Momentum is building, with growing calls for digital independence from leaders like Ministers Olsen and Wildberger.  Initiatives such as the Digital Europe Programme, which seeks to reduce reliance on foreign systems and solutions, further accelerate this push. As a result, the EU’s transition could arrive sooner rather than later As technology continues to evolve—from the return of 'dumbphones' to faster and sleeker computers—seasoned tech journalist, Cedric Solidon, continues to dedicate himself to writing stories that inform, empower, and connect with readers across all levels of digital literacy. With 20 years of professional writing experience, this University of the Philippines Journalism graduate has carved out a niche as a trusted voice in tech media. Whether he's breaking down the latest advancements in cybersecurity or explaining how silicon-carbon batteries can extend your phone’s battery life, his writing remains rooted in clarity, curiosity, and utility. Long before he was writing for Techreport, HP, Citrix, SAP, Globe Telecom, CyberGhost VPN, and ExpressVPN, Cedric's love for technology began at home courtesy of a Nintendo Family Computer and a stack of tech magazines. Growing up, his days were often filled with sessions of Contra, Bomberman, Red Alert 2, and the criminally underrated Crusader: No Regret. But gaming wasn't his only gateway to tech.  He devoured every T3, PCMag, and PC Gamer issue he could get his hands on, often reading them cover to cover. It wasn’t long before he explored the early web in IRC chatrooms, online forums, and fledgling tech blogs, soaking in every byte of knowledge from the late '90s and early 2000s internet boom. That fascination with tech didn’t just stick. It evolved into a full-blown calling. After graduating with a degree in Journalism, he began his writing career at the dawn of Web 2.0. What started with small editorial roles and freelance gigs soon grew into a full-fledged career. He has since collaborated with global tech leaders, lending his voice to content that bridges technical expertise with everyday usability. He’s also written annual reports for Globe Telecom and consumer-friendly guides for VPN companies like CyberGhost and ExpressVPN, empowering readers to understand the importance of digital privacy. His versatility spans not just tech journalism but also technical writing. He once worked with a local tech company developing web and mobile apps for logistics firms, crafting documentation and communication materials that brought together user-friendliness with deep technical understanding. That experience sharpened his ability to break down dense, often jargon-heavy material into content that speaks clearly to both developers and decision-makers. At the heart of his work lies a simple belief: technology should feel empowering, not intimidating. Even if the likes of smartphones and AI are now commonplace, he understands that there's still a knowledge gap, especially when it comes to hardware or the real-world benefits of new tools. His writing hopes to help close that gap. Cedric’s writing style reflects that mission. It’s friendly without being fluffy and informative without being overwhelming. Whether writing for seasoned IT professionals or casual readers curious about the latest gadgets, he focuses on how a piece of technology can improve our lives, boost our productivity, or make our work more efficient. That human-first approach makes his content feel more like a conversation than a technical manual. As his writing career progresses, his passion for tech journalism remains as strong as ever. With the growing need for accessible, responsible tech communication, he sees his role not just as a journalist but as a guide who helps readers navigate a digital world that’s often as confusing as it is exciting. From reviewing the latest devices to unpacking global tech trends, Cedric isn’t just reporting on the future; he’s helping to write it. View all articles by Cedric Solidon Our editorial process The Tech Report editorial policy is centered on providing helpful, accurate content that offers real value to our readers. We only work with experienced writers who have specific knowledge in the topics they cover, including latest developments in technology, online privacy, cryptocurrencies, software, and more. Our editorial policy ensures that each topic is researched and curated by our in-house editors. We maintain rigorous journalistic standards, and every article is 100% written by real authors.

There is no good without bad. It’s a cliché, but it’s true. How can you fully appreciate an exceptional work of art without comparing it to one that didn’t work? A truly awful movie puts a truly great masterpiece into perspective.So consider this piece a study in perspectives. No one who made any of the 20 movies below, our picks for the 20 worst movies of the last 20 years, set out to produce a bad movie. But it happened anyway, despite all their hard work and good intentions. Writing is hard. Casting is hard. Directing is hard. Movies are hard.(Okay, strike that. At least one filmmaker on the list reportedly exploited a tax loophole that meant investors only had to pay taxes on investments in films that turned a profit, leaving a financial incentive for a movie to flop. So maybe someone occasionally does set out to make a bad movie. Or at least, the movie’s quality is of far lesser concern than, say, sales to foreign distributors. But it’s rare.)If you’re thinking about using this list to help program your friends’ next Bad Movie Night, just keep in mind: Some of the films below are not so-bad-they’re-good. They’re just plain awful. (The tax loophole guy’s film, for example, that’s a real tough sit.) Proceed with caution and remember: There is no good without bad ... but sometimes it’s better to just watch a good movie instead of forcing the comparison.The 20 Worst Movies of the Last 20 Years (2005-2024)Movies can bring us to the highest highs and the lowest lows. These 20 films of the last 20 years are very much the latter.READ MORE: 25 Actors Who Turned Down Roles in Huge MoviesGet our free mobile appThe 20 Best Movies of the Last 20 Years (2005-2024)The 20 films of the last two decades that you absolutely need to see.Categories: Lists, Longform, Movie News, Special Features

This month, join forces to tackle the paranormal crises of a mysterious federal agency under siege in the cooperative first-person shooter FBC: Firebreak, lead your team to victory in the iconic all-out warfare of Battlefield 2042, test your skills as a new Fazbear employee managing and maintaining the eerie pizzeria of Five Nights at Freddy’s: Help Wanted 2 or live for the thrill of the hunt in the realistic hunting open world theHunter: Call of the Wild. All of these titles and more are available in June’s PlayStation Plus Game Catalog lineup*.    Meanwhile, PS2’s Deus Ex: The Conspiracy merges action-RPG, stealth and FPS gameplay in PlayStation Plus Premium.    All titles will be available to play on June 17.   PlayStation Plus Extra and Premium | Game Catalog  View and download image Download the image close Close Download this image FBC: Firebreak | PS5 Launching on the PlayStation Plus Game Catalog this month is FBC: Firebreak, a cooperative first-person shooter set within a mysterious federal agency under assault by otherworldly forces. Return to the strange and unexpected world of Control or venture in for the first time in this standalone, multiplayer experience. As a years-long siege on the agency’s headquarters reaches its boiling point, only Firebreak—the Bureau’s most versatile unit—has the gear and the guts to plunge into the building’s strangest crises, restore order, contain the chaos, and fight to reclaim control. Join forces with friends or strangers to tackle each job as a well-oiled crew. Survival in this three-player cooperative FPS hinges on quick thinking and seamless teamwork as you scramble to tame raging paranatural crises across a variety of unexpected locations.    View and download image Download the image close Close Download this image Battlefield 2042 | PS4, PS5 Battlefield 2042 is a first-person shooter that marks the return to the iconic all-out warfare of the franchise. With the help of a cutting-edge arsenal, engage in intense, immersive multiplayer battles. Lead your team to victory in both large all-out warfare and close quarters combat on maps from the world of 2042 and classic Battlefield titles. Find your playstyle in class-based gameplay and take on several experiences comprising elevated versions of Conquest and Breakthrough. Explore Battlefield Portal, a platform where players can discover, create, and share unexpected battles from Battlefield’s past and present. View and download image Download the image close Close Download this image Five Nights at Freddy’s: Help Wanted 2 | PS5 Five Nights at Freddy’s: Help Wanted 2 is the sequel to the terrifying VR experience that brought new life to the iconic horror franchise. As a brand new Fazbear employee you’ll have to prove you have what it takes to excel in all aspects of Pizzeria management and maintenance. Find out if you have what it takes to be a Fazbear Entertainment Superstar! View and download image Download the image close Close Download this image theHunter: Call of the Wild | PS4 Discover an atmospheric hunting game like no other in this realistic, stunning open world – regularly updated in collaboration with its community. Immerse yourself in the single player campaign, or share the ultimate hunting experience with friends. Roam freely across meticulously crafted environments and explore a diverse range of regions and biomes, each with its own unique flora and fauna. Experience the intricacies of complex animal behavior, dynamic weather events, full day and night cycles, simulated ballistics, highly realistic acoustics, and scents carried by the wind. Select from a variety of weapons, ammunition, and equipment to create the ultimate hunting experience. With a diverse range of wildlife, including Jackrabbits, Mallard Ducks, Black Bears, Elk, and Moose, you will need to strategically match prey to weaponry to successfully track, lure, and ambush animals based on their unique behavior and environment. View and download image Download the image close Close Download this image We Love Katamari Reroll + Royal Reverie | PS4, PS5 We Love Katamari Damacy, the second title in the Katamari series released in 2005, has been remastered with redesigned graphics and a revamped in-game UI. The King of the Cosmos accidentally destroyed all the stars in the universe. He sent his son, the Prince, to Earth and ordered him to create a large katamari. Roll the katamari to make it bigger and bigger, rolling up all the things on the earth. You can roll up anything from paper clips and snacks in the house, to telephone poles and buildings in the town, to even living creatures such as people and animals. Once the katamari is complete, it will turn into a star that colors the night sky. You cannot roll up anything larger than the current size of your katamari, so the key is to think in advance about the order in which you roll things up around the stage. In Royal Reverie, roll up katamari as the King of All Cosmos in his boyhood! View and download image Download the image close Close Download this image Eiyuden Chronicles: Hundred Heroes | PS4, PS5 Directed and produced by the creator of treasured JRPG series Suikoden, Eiyuden Chronicle: Hundred Heroes provides a contemporary take on the classic JRPG experience. In the land of Allraan, two friends from different backgrounds are united by a war waged by the power-hungry Galdean Empire. Explore a diverse, magical world populated by humans, beastmen, elves and desert people. Meet and recruit over 100 unique characters, each with their own vivid voice acting and intricate backstories. Over four years in the making, and funded by the most successful Kickstarter videogame campaign of 2020, Eiyuden Chronicle: Hundred Heroes features turn-based battles, a staggering selection of heroes and a thrilling story to discover. View and download image Download the image close Close Download this image Train Sim World 5 | PS4, PS5 The rails are yours in Train Sim World 5! Take on new challenges and new roles as you master the tracks and trains of iconic cities across 3 new routes. Immerse yourself in the ultimate rail hobby and embark on your next journey. Be swept off your feet with the commuter mayhem of the West Coast main line with the Northwestern Class 350, the twisting Kinzigtalbahn with the tilting DB BR 411 ICE-T, or the sun-soaked tracks of the San Bernardino line and its Metrolink movements, powered by the MP36 & F125.  View and download image Download the image close Close Download this image Endless Dungeon | PS4, PS5 Endless Dungeon is a unique blend of roguelite, tactical action, and tower defense set in the award-winning Endless Universe. Plunge into an abandoned space station alone or with friends in co-op, recruit a team of shipwrecked heroes, and protect your crystal against never-ending waves of monsters… or die trying, get reloaded, and try again. You’re stranded on an abandoned space station chock-full of monsters and mysteries. To get out you’ll have to reach The Core, but you can’t do that without your crystal bot. That scuttling critter is your key to surviving the procedurally generated rooms of this space ruin. Sadly, it’s also a fragile soul, and every monster in the place wants a piece of it. You’re going to have to think quick, plan well, place your turrets, and then… fireworks! Bugs, bots and blobs will stop at nothing to turn you and that crystal into dust and debris. With a large choice of weapons and turrets, the right gear will be the difference between life and death. PlayStation Plus Premium  View and download image Download the image close Close Download this image Deus Ex: The Conspiracy | PS4, PS5This is an emulation of the classic PS2 title, Deus Ex: The Conspiracy, playable on PS4 and PS5 for the first time. The year is 2052 and the world is a dangerous and chaotic place. Terrorists operate openly – killing thousands; drugs, disease and pollution kill even more. The world’s economies are close to collapse and the gap between the insanely wealthy and the desperately poor grows ever wider. Worst of all, an age- old conspiracy bent on world domination has decided that the time is right to emerge from the shadows and take control.  *PlayStation Plus Game Catalog and PlayStation Plus Premium/Deluxe lineups may differ by region. Please check PlayStation Store on release day. 

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]