Exciting news for iPhone enthusiasts! While rumors suggest that the iPhone 17 and iPhone 17 Air might feature 120Hz screens, it seems they won’t come with the ProMotion technology that many of us have come to love. This raises a crucial question: will the lack of ProMotion impact our daily experience, or is it just a minor detail? From my perspective, the smoothness of scrolling and gaming on ProMotion displays is a game changer. However, if Apple delivers on other performance aspects, it might still make the iPhone 17 Air a worthy contender in the market. What do you think—are you willing to sacrifice ProMotion for potentially other great features? Let's discuss!

One of the key use cases for generative AI involves answering questions over private datasets, with retrieval-augmented generation as the go-to framework. As new RAG techniques emerge, there’s a growing need to benchmark their performance across diverse datasets and metrics.  To meet this need, we’re introducing BenchmarkQED, a new suite of tools that automates RAG benchmarking at scale, available on GitHub. It includes components for query generation, evaluation, and dataset preparation, each designed to support rigorous, reproducible testing.   BenchmarkQED complements the RAG methods in our open-source GraphRAG library, enabling users to run a GraphRAG-style evaluation across models, metrics, and datasets. GraphRAG uses a large language model to generate and summarize entity-based knowledge graphs, producing more comprehensive and diverse answers than standard RAG for large-scale tasks.  In this post, we walk through the core components of BenchmarkQED that contribute to the overall benchmarking process. We also share some of the latest benchmark results comparing our LazyGraphRAG system to competing methods, including a vector-based RAG with a 1M-token context window, where the leading LazyGraphRAG configuration showed significant win rates across all combinations of quality metrics and query classes. In the paper, we distinguish between local queries, where answers are found in a small number of text regions, and sometimes even a single region, and global queries, which require reasoning over large portions of or even the entire dataset.  Conventional vector-based RAG excels at local queries because the regions containing the answer to the query resemble the query itself and can be retrieved as the nearest neighbor in the vector space of text embeddings. However, it struggles with global questions, such as, “What are the main themes of the dataset?” which require understanding dataset qualities not explicitly stated in the text.   AutoQ: Automated query synthesis This limitation motivated the development of GraphRAG a system designed to answer global queries. GraphRAG’s evaluation requirements subsequently led to the creation of AutoQ, a method for synthesizing these global queries for any dataset. AutoQ extends this approach by generating synthetic queries across the spectrum of queries, from local to global. It defines four distinct classes based on the source and scope of the queryforming a logical progression along the spectrum. Figure 1. Construction of a 2×2 design space for synthetic query generation with AutoQ, showing how the four resulting query classes map onto the local-global query spectrum.  AutoQ can be configured to generate any number and distribution of synthetic queries along these classes, enabling consistent benchmarking across datasets without requiring user customization. Figure 2 shows the synthesis process and sample queries from each class, using an AP News dataset. Figure 2. Synthesis process and example query for each of the four AutoQ query classes.  About Microsoft Research Advancing science and technology to benefit humanity View our story Opens in a new tab AutoE: Automated evaluation framework  Our evaluation of GraphRAG focused on analyzing key qualities of answers to global questions. The following qualities were used for the current evaluation: Comprehensiveness: Does the answer address all relevant aspects of the question?  Diversity: Does it present varied perspectives or insights?  Empowerment: Does it help the reader understand and make informed judgments?  Relevance: Does it address what the question is specifically asking?   The AutoE component scales evaluation of these qualities using the LLM-as-a-Judge method. It presents pairs of answers to an LLM, along with the query and target metric, in counterbalanced order. The model determines whether the first answer wins, loses, or ties with the second. Over a set of queries, whether from AutoQ or elsewhere, this produces win rates between competing methods. When ground truth is available, AutoE can also score answers on correctness, completeness, and related metrics. An illustrative evaluation is shown in Figure 3. Using a dataset of 1,397 AP News articles on health and healthcare, AutoQ generated 50 queries per class . AutoE then compared LazyGraphRAG to a competing RAG method, running six trials per query across four metrics, using GPT-4.1 as a judge. These trial-level results were aggregated using metric-based win rates, where each trial is scored 1 for a win, 0.5 for a tie, and 0 for a loss, and then averaged to calculate the overall win rate for each RAG method. Figure 3. Win rates of four LazyGraphRAG configurations across methods, broken down by the AutoQ query class and averaged across AutoE’s four metrics: comprehensiveness, diversity, empowerment, and relevance. LazyGraphRAG outperforms comparison conditions where the bar is above 50%. The four LazyGraphRAG conditionsdiffer by query budgetand chunk size. All used GPT-4o mini for relevance tests and GPT-4o for query expansionand answer generation, except for LGR_b200_c200_mini, which used GPT-4o mini throughout. Comparison systems were GraphRAG , Vector RAG with 8k- and 120k-token windows, and three published methods: LightRAG, RAPTOR, and TREX. All methods were limited to the same 8k tokens for answer generation. GraphRAG Global Search used level 2 of the community hierarchy. LazyGraphRAG outperformed every comparison condition using the same generative model, winning all 96 comparisons, with all but one reaching statistical significance. The best overall performance came from the larger budget, smaller chunk size configuration. For DataLocal queries, the smaller budgetperformed slightly better, likely because fewer chunks were relevant. For ActivityLocal queries, the larger chunk sizehad a slight edge, likely because longer chunks provide a more coherent context. Competing methods performed relatively better on the query classes for which they were designed: GraphRAG Global for global queries, Vector RAG for local queries, and GraphRAG Drift Search, which combines both strategies, posed the strongest challenge overall. Increasing Vector RAG’s context window from 8k to 120k tokens did not improve its performance compared to LazyGraphRAG. This raised the question of how LazyGraphRAG would perform against Vector RAG with 1-million token context window containing most of the dataset. Figure 4 shows the follow-up experiment comparing LazyGraphRAG to Vector RAG using GPT-4.1 that enabled this comparison. Even against the 1M-token window, LazyGraphRAG achieved higher win rates across all comparisons, failing to reach significance only for the relevance of answers to DataLocal queries. These queries tend to benefit most from Vector RAG’s ranking of directly relevant chunks, making it hard for LazyGraphRAG to generate answers that have greater relevance to the query, even though these answers may be dramatically more comprehensive, diverse, and empowering overall. Figure 4. Win rates of LazyGraphRAG  over Vector RAG across different context window sizes, broken down by the four AutoQ query classes and four AutoE metrics: comprehensiveness, diversity, empowerment, and relevance. Bars above 50% indicate that LazyGraphRAG outperformed the comparison condition.  AutoD: Automated data sampling and summarization Text datasets have an underlying topical structure, but the depth, breadth, and connectivity of that structure can vary widely. This variability makes it difficult to evaluate RAG systems consistently, as results may reflect the idiosyncrasies of the dataset rather than the system’s general capabilities. The AutoD component addresses this by sampling datasets to meet a target specification, defined by the number of topic clustersand the number of samples per cluster. This creates consistency across datasets, enabling more meaningful comparisons, as structurally aligned datasets lead to comparable AutoQ queries, which in turn support consistent AutoE evaluations. AutoD also includes tools for summarizing input or output datasets in a way that reflects their topical coverage. These summaries play an important role in the AutoQ query synthesis process, but they can also be used more broadly, such as in prompts where context space is limited. Since the release of the GraphRAG paper, we’ve received many requests to share the dataset of the Behind the Tech podcast transcripts we used in our evaluation. An updated version of this dataset is now available in the BenchmarkQED repository, alongside the AP News dataset containing 1,397 health-related articles, licensed for open release.   We hope these datasets, together with the BenchmarkQED tools, help accelerate benchmark-driven development of RAG systems and AI question-answering. We invite the community to try them on GitHub.  Opens in a new tab #benchmarkqedautomatedbenchmarking #ofrag #systems

Last year, China saw a boom in foundation models, the do-everything large language models that underpin the AI revolution. This year, the focus has shifted to AI agents—systems that are less about responding to users’ queries and more about autonomously accomplishing things for them.  There are now a host of Chinese startups building these general-purpose digital tools, which can answer emails, browse the internet to plan vacations, and even design an interactive website. Many of these have emerged in just the last two months, following in the footsteps of Manus—a general AI agent that sparked weeks of social media frenzy for invite codes after its limited-release launch in early March.  These emerging AI agents aren’t large language models themselves. Instead, they’re built on top of them, using a workflow-based structure designed to get things done. A lot of these systems also introduce a different way of interacting with AI. Rather than just chatting back and forth with users, they are optimized for managing and executing multistep tasks—booking flights, managing schedules, conducting research—by using external tools and remembering instructions.  China could take the lead on building these kinds of agents. The country’s tightly integrated app ecosystems, rapid product cycles, and digitally fluent user base could provide a favorable environment for embedding AI into daily life.  For now, its leading AI agent startups are focusing their attention on the global market, because the best Western models don’t operate inside China’s firewalls. But that could change soon: Tech giants like ByteDance and Tencent are preparing their own AI agents that could bake automation directly into their native super-apps, pulling data from their vast ecosystem of programs that dominate many aspects of daily life in the country.  As the race to define what a useful AI agent looks like unfolds, a mix of ambitious startups and entrenched tech giants are now testing how these tools might actually work in practice—and for whom. Set the standard It’s been a whirlwind few months for Manus, which was developed by the Wuhan-based startup Butterfly Effect. The company raised million in a funding round led by the US venture capital firm Benchmark, took the product on an ambitious global roadshow, and hired dozens of new employees.  Even before registration opened to the public in May, Manus had become a reference point for what a broad, consumer‑oriented AI agent should accomplish. Rather than handling narrow chores for businesses, this “general” agent is designed to be able to help with everyday tasks like trip planning, stock comparison, or your kid’s school project.  Unlike previous AI agents, Manus uses a browser-based sandbox that lets users supervise the agent like an intern, watching in real time as it scrolls through web pages, reads articles, or codes actions. It also proactively asks clarifying questions, supports long-term memory that would serve as context for future tasks. “Manus represents a promising product experience for AI agents,” says Ang Li, cofounder and CEO of Simular, a startup based in Palo Alto, California, that’s building computer use agents, AI agents that control a virtual computer. “I believe Chinese startups have a huge advantage when it comes to designing consumer products, thanks to cutthroat domestic competition that leads to fast execution and greater attention to product details.” In the case of Manus, the competition is moving fast. Two of the most buzzy follow‑ups, Genspark and Flowith, for example, are already boasting benchmark scores that match or edge past Manus’s.  Genspark, led by former Baidu executives Eric Jing and Kay Zhu, links many small “super agents” through what it calls multi‑component prompting. The agent can switch among several large language models, accepts both images and text, and carries out tasks from making slide decks to placing phone calls. Whereas Manus relies heavily on Browser Use, a popular open-source product that lets agents operate a web browser in a virtual window like a human, Genspark directly integrates with a wide array of tools and APIs. Launched in April, the company says that it already has over 5 million users and over million in yearly revenue. Flowith, the work of a young team that first grabbed public attention in April 2025 at a developer event hosted by the popular social media app Xiaohongshu, takes a different tack. Marketed as an “infinite agent,” it opens on a blank canvas where each question becomes a node on a branching map. Users can backtrack, take new branches, and store results in personal or sharable “knowledge gardens”—a design that feels more like project management softwarethan a typical chat interface. Every inquiry or task builds its own mind-map-like graph, encouraging a more nonlinear and creative interaction with AI. Flowith’s core agent, NEO, runs in the cloud and can perform scheduled tasks like sending emails and compiling files. The founders want the app to be a “knowledge marketbase”, and aims to tap into the social aspect of AI with the aspiration of becoming “the OnlyFans of AI knowledge creators”. What they also share with Manus is the global ambition. Both Genspark and Flowith have stated that their primary focus is the international market. A global address Startups like Manus, Genspark, and Flowith—though founded by Chinese entrepreneurs—could blend seamlessly into the global tech scene and compete effectively abroad. Founders, investors, and analysts that MIT Technology Review has spoken to believe Chinese companies are moving fast, executing well, and quickly coming up with new products.  Money reinforces the pull to launch overseas. Customers there pay more, and there are plenty to go around. “You can price in USD, and with the exchange rate that’s a sevenfold multiplier,” Manus cofounder Xiao Hong quipped on a podcast. “Even if we’re only operating at 10% power because of cultural differences overseas, we’ll still make more than in China.” But creating the same functionality in China is a challenge. Major US AI companies including OpenAI and Anthropic have opted out of mainland China because of geopolitical risks and challenges with regulatory compliance. Their absence initially created a black market as users resorted to VPNs and third-party mirrors to access tools like ChatGPT and Claude. That vacuum has since been filled by a new wave of Chinese chatbots—DeepSeek, Doubao, Kimi—but the appetite for foreign models hasn’t gone away.  Manus, for example, uses Anthropic’s Claude Sonnet—widely considered the top model for agentic tasks. Manus cofounder Zhang Tao has repeatedly praised Claude’s ability to juggle tools, remember contexts, and hold multi‑round conversations—all crucial for turning chatty software into an effective executive assistant. But the company’s use of Sonnet has made its agent functionally unusable inside China without a VPN. If you open Manus from a mainland IP address, you’ll see a notice explaining that the team is “working on integrating Qwen’s model,” a special local version that is built on top of Alibaba’s open-source model.  An engineer overseeing ByteDance’s work on developing an agent, who spoke to MIT Technology Review anonymously to avoid sanction, said that the absence of Claude Sonnet models “limits everything we do in China.” DeepSeek’s open models, he added, still hallucinate too often and lack training on real‑world workflows. Developers we spoke with rank Alibaba’s Qwen series as the best domestic alternative, yet most say that switching to Qwen knocks performance down a notch. Jiaxin Pei, a postdoctoral researcher at Stanford’s Institute for Human‑Centered AI, thinks that gap will close: “Building agentic capabilities in base LLMs has become a key focus for many LLM builders, and once people realize the value of this, it will only be a matter of time.” For now, Manus is doubling down on audiences it can already serve. In a written response, the company said its “primary focus is overseas expansion,” noting that new offices in San Francisco, Singapore, and Tokyo have opened in the past month. A super‑app approach Although the concept of AI agents is still relatively new, the consumer-facing AI app market in China is already crowded with major tech players. DeepSeek remains the most widely used, while ByteDance’s Doubao and Moonshot’s Kimi have also become household names. However, most of these apps are still optimized for chat and entertainment rather than task execution. This gap in the local market has pushed China’s big tech firms to roll out their own user-facing agents, though early versions remain uneven in quality and rough around the edges.  ByteDance is testing Coze Space, an AI agent based on its own Doubao model family that lets users toggle between “plan” and “execute” modes, so they can either directly guide the agent’s actions or step back and watch it work autonomously. It connects up to 14 popular apps, including GitHub, Notion, and the company’s own Lark office suite. Early reviews say the tool can feel clunky and has a high failure rate, but it clearly aims to match what Manus offers. Meanwhile, Zhipu AI has released a free agent called AutoGLM Rumination, built on its proprietary ChatGLM models. Shanghai‑based Minimax has launched Minimax Agent. Both products look almost identical to Manus and demo basic tasks such as building a simple website, planning a trip, making a small Flash game, or running quick data analysis. Despite the limited usability of most general AI agents launched within China, big companies have plans to change that. During a May 15 earnings call, Tencent president Liu Zhiping teased an agent that would weave automation directly into China’s most ubiquitous app, WeChat.  Considered the original super-app, WeChat already handles messaging, mobile payments, news, and millions of mini‑programs that act like embedded apps. These programs give Tencent, its developer, access to data from millions of services that pervade everyday life in China, an advantage most competitors can only envy. Historically, China’s consumer internet has splintered into competing walled gardens—share a Taobao link in WeChat and it resolves as plaintext, not a preview card. Unlike the more interoperable Western internet, China’s tech giants have long resisted integration with one another, choosing to wage platform war at the expense of a seamless user experience. But the use of mini‑programs has given WeChat unprecedented reach across services that once resisted interoperability, from gym bookings to grocery orders. An agent able to roam that ecosystem could bypass the integration headaches dogging independent startups. Alibaba, the e-commerce giant behind the Qwen model series, has been a front-runner in China’s AI race but has been slower to release consumer-facing products. Even though Qwen was the most downloaded open-source model on Hugging Face in 2024, it didn’t power a dedicated chatbot app until early 2025. In March, Alibaba rebranded its cloud storage and search app Quark into an all-in-one AI search tool. By June, Quark had introduced DeepResearch—a new mode that marks its most agent-like effort to date.  ByteDance and Alibaba did not reply to MIT Technology Review’s request for comments. “Historically, Chinese tech products tend to pursue the all-in-one, super-app approach, and the latest Chinese AI agents reflect just that,” says Li of Simular, who previously worked at Google DeepMind on AI-enabled work automation. “In contrast, AI agents in the US are more focused on serving specific verticals.” Pei, the researcher at Stanford, says that existing tech giants could have a huge advantage in bringing the vision of general AI agents to life—especially those with built-in integration across services. “The customer-facing AI agent market is still very early, with tons of problems like authentication and liability,” he says. “But companies that already operate across a wide range of services have a natural advantage in deploying agents at scale.” #manus #has #kickstarted #agent #boom

Exciting times ahead for IT management as Jamf integrates AI into Apple device management! With the rapid rise of generative AI, it's fascinating to see how it’s transforming not just our daily lives but also the tools we use in enterprise settings. Jamf's latest features unveiled at Jamf Nation Live promise to revolutionize how we manage Apple devices, making processes more efficient and user-friendly. As a Compositing Artist, I can appreciate the artistry that goes into blending technology and creativity—just like how AI can enhance productivity while allowing us to focus on the more imaginative aspects of our work. How do you envision AI changing the landscape of IT management in the coming years? #Jamf #AI #AppleManagement #TechInnovation #GenerativeAI

In the fast-paced world of cybersecurity, burnout is becoming an alarming reality for many experts. However, the rise of AI technology presents an exciting opportunity to safeguard their mental health. By automating routine tasks and enhancing threat detection, AI not only alleviates the pressure but also allows professionals to focus on more strategic aspects of their work. As someone who’s passionate about the intersection of technology and well-being, I believe integrating AI into our daily routines can be transformative—not just for productivity, but for mental resilience too. Embracing these innovations could be the key to building a healthier work environment in high-stress fields. Let’s champion a future where technology supports our mind as much as it protects our systems! #MentalHealth #Cybersecurity #AI #WorkLifeBalance #TechForGood

06/03 — 2025 by abduzeedo Explore the impactful branding and visual identity for Culture & Community Power Fund by Outside. See how design elevates community action. Hey creatives! Let's dive into some truly inspiring work today. Our friends at Outside, a global design and tech studio based in Kathmandu, recently rolled out a fresh brand and website for the Culture & Community Power Fund. This project isn't just about aesthetics; it's about design with a deeper purpose. Crafting a Visual Voice for Community Power C&CPF champions grassroots organizations. They focus on communities most affected by systemic oppression. Their mission involves direct funding, sharing resources, and building networks. Outside's goal was clear: uplift grantee work, create a wisdom hub, and clarify culture's role in social action . Elizabeth Lepro, Editorial Lead at Outside, shared that the project expanded their understanding of "culture." They aimed to visually inject the celebratory and communal aspects of shared spaces, rituals, stories, and traditions throughout the brand . This passion shines through in the final output. The Ampersand: A Symbol of Collaboration One standout element in C&CPF's new branding and visual identity is the ampersand. Outside emphasized this symbol in the logo, a direct nod to C&CPF's collaborative ethos. What's really clever is how they rendered the ampersand in various fonts. Each typeface reflects the diverse visions of C&CPF's partner-grantees. For instance, the rounded Akaya Kanadaka typeface conveys the joyful spirit of culturally infused community power building. Pilowlava, another typeface, speaks to a grantee-partner's work in urban farming and sustainability . It's a subtle yet powerful way to represent unity in diversity. Gritty, Bold, and Inviting Visuals Established in 2022, C&CPF is relatively new. They wanted to avoid looking overly traditional. Outside delivered a gritty, bold aesthetic, drawing inspiration from protest signs and zines. Snappy animations complement this vibe. They even provided image frames to unify varying aesthetics from featured organizations. The chosen color palette is a rich blend of orange, purple, yellow, black, and white. This combination expresses warmth and invitation, perfectly aligning with the Fund's focus on people and communities. The brand incorporates vibrant photography, showcasing scenes like festivals, local games, and urban farms. Bridging the Knowledge Gap Beyond the visual, Outside also provided copywriting and narrative support. They made sure the site explains "community power building" in accessible language. The "What is Community Power Building?" page breaks down basics, relevant vocabulary, and context. It clarifies that culture extends beyond art and music, encompassing traditions, stories, rituals, and shared spaces. A key feature is the "Knowledge Commons," a custom-built resource library. It serves both experienced practitioners and newcomers, fostering shared wisdom. This hub even accepts submissions, allowing C&CPF team members to edit and publish them. This project by Outside is a fantastic example of how thoughtful branding and visual identity can empower a mission. It shows that design isn't just about looking good; it's about building understanding, fostering connection, and driving meaningful change. What aspects of this purposeful design resonate most with you? Explore more of Outside's work and the Culture & Community Power Fund's initiatives to see how design can truly make an impact. Learn more about Outside's work Branding and visual identity artifacts Tags branding #purposeful #design #campampcpf039s #bold #new

Matali Physics 6.9 Presents Physics-driven, Completely Dynamic Light Sources Published June 02, 2025 Advertisement We are pleased to announce the release of Matali Physics 6.9, the next significant step on the way to the seventh major version of the environment. Matali Physics 6.9 introduces a number of improvements and fixes to Matali Physics Core, Matali Render and Matali Games modules, presents physics-driven, completely dynamic light sources, real-time object scaling with destruction, lighting model simulating global illuminationin some aspects, comprehensive support for Wayland on Linux, improved simulation of stacked objects, improved physics-based photo mode, introduces optimizations for Steam Deck portable gaming console and Linux as well as provides a refreshed Matali Physics Game demo. What is Matali Physics?Matali Physics is an advanced, modern, multi-platform, high-performance 3d physics environment intended for games, VR, AR, physics-based simulations and robotics. Matali Physics consists of the advanced 3d physics engine Matali Physics Core and other physics-driven modules that all together provide comprehensive simulation of physical phenomena and physics-based modeling of both real and imaginary objects.What's new in version 6.9?Physics-driven, completely dynamic light sources. The introduced solution allows for processing hundreds of movable, long-range and shadow-casting light sources, where with each source can be assigned logic that controls its behavior, changes light parameters, volumetric effects parameters and others;Real-time object scaling with destruction. All groups of physics objects and groups of physics objects with constraints may be subject to destruction process during real-time scaling, allowing group members to break off at different sizes;Lighting model simulating global illuminationin some aspects. Based on own research and development work, processed in real time, ready for dynamic scenes, fast on mobile devices, not based on lightmaps, light probes, baked lights, etc.;Comprehensive support for Wayland on Linux. The latest version allows Matali Physics SDK users to create advanced, high-performance, physics-based, Vulkan-based games for modern Linux distributions where Wayland is the main display server protocol;Other improvements and fixes which complete list is available on the History webpage.What platforms does Matali Physics support?AndroidAndroid TV*BSDiOSiPadOSLinuxmacOSSteam DecktvOSUWPWindowsWhat are the benefits of using Matali Physics?Physics simulation, graphics, sound and music integrated into one total multimedia solution where creating complex interactions and behaviors is common and relatively easyComposed of dedicated modules that do not require additional licences and feesSupports fully dynamic and destructible scenesSupports physics-based behavioral animationsSupports physical AI, object motion and state change controlSupports physics-based GUISupports physics-based particle effectsSupports multi-scene physics simulation and scene combiningSupports physics-based photo modeSupports physics-driven soundSupports physics-driven musicSupports debug visualizationFully serializable and deserializableAvailable for all major mobile, desktop and TV platformsNew features on requestDedicated technical supportRegular updates and fixesIf you have questions related to the latest version and the use of Matali Physics environment as a game creation solution, please do not hesitate to contact us.  Cancel 0 likes 0 comments Comments Nobody has left a comment. You can be the first! You must log in to join the conversation. Don't have a GameDev.net account? Sign up! #matali #physics #presents #physicsdriven #completely

CIOs and business leaders know they’re sitting on a goldmine of business data. And while traditional tools such as business intelligence platforms and statistical analysis software can effectively surface insights from the collated data resources, doing so quickly, in real-time and at scale remains an unsolved challenge.Enterprise AI, when deployed responsibly and at scale, can turn these bottlenecks into opportunities. Acting quickly on data, even ‘live’, is one of the technology’s abilities, as is scalability: AI can process large amounts of information from disparate sources almost as easily as it can summarize a one-page spreadsheet.But deploying an AI solution in the modern enterprise isn’t simple. It takes structure, trust and the right talent. Along with the practical implementation challenges, using AI brings its own challenges, such as data governance, the need to impose guardrails on AI responses and training data, and persistent staffing issues.We met with Rani Radhakrishnan, PwC Principal, Technology Managed Services – AI, Data Analytics and Insights, to talk candidly about what’s working — and what’s holding back CIOs in their AI journey. We spoke ahead of her speaking engagement at TechEx AI & Big Data Expo North America, June 4 and 5, at the Santa Clara Convention Center.Rani is especially attuned to some of the governance, data privacy and sovereignty issues that face enterprises, having spent many years in her career working with numerous clients in the health sector — an area where issues like privacy, data oversight and above all data accuracy are make-or-break aspects of technology deployments.“It’s not enough to just have a prompt engineer or a Python developer. … You still need the human in the loop to curate the right training data sets, review and address any bias in the outputs.” —Rani Radhakrishnan, PwCFrom support to strategy: shifting expectations for AIRani said that there’s a growing enthusiasm from PwC’s clients for AI-powered managed services that can provide both business insights in every sector, and for the technology to be used more proactively, in so-called agentic roles where agents can independently act on data and user input; where autonomous AI agents can take action based on interactions with humans, access to data resources and automation.For example, PwC’s agent OS is a modular AI platform that connects systems and scales intelligent agents into workflows, many times faster than traditional computing methods. It’s an example of how PwC responds to the demand for AI from its clients, many of whom see the potential of this new technology, but lack the in-house expertise and staff to act on their needs.Depending on the sector of the organization, the interest in AI can come from many different places in the business. Proactive monitoring of physical or digital systems; predictive maintenance in manufacturing or engineering; or cost efficiencies won by automation in complex, customer-facing environments, are just a few examples.But regardless of where AI can bring value, most companies don’t yet have in-house the range of skills and people necessary for effective AI deployment — or at least, deployments that achieve ROI and don’t come with significant risk.“It’s not enough to just have a prompt engineer or a Python developer,” Rani said. “You’ve got to put all of these together in a very structured manner, and you still need the human in the loop to curate the right training data sets, review and address any bias in the outputs.”Cleaning house: the data challenge behind AIRani says that effective AI implementations need a mix of technical skills — data engineering, data science, prompt engineering — in combination with an organization’s domain expertise. Internal domain expertise can define the right outcomes, and technical staff can cover the responsible AI practices, like data collation and governance, and confirm that AI systems work responsibly and within company guidelines.“In order to get the most value out of AI, an organization has to get the underlying data right,” she said. “I don’t know of a single company that says its data is in great shape … you’ve got to get it into the right structure and normalize it properly so you can query, analyze, and annotate it and identify emerging trends.”Part of the work enterprises have to put in for effective AI use is the observation for and correction of bias — in both output of AI systems and in the analysis of potential bias inherent in training and operational data.It’s important that as part of the underlying architecture of AI systems, teams apply stringent data sanitization, normalization, and data annotation processes. The latter requires “a lot of human effort,” Rani said, and the skilled personnel required are among the new breed of data professionals that are beginning to emerge.If data and personnel challenges can be overcome, then the feedback loop makes the possible outcomes from generative AI really valuable, Rani said. “Now you have an opportunity with AI prompts to go back and refine the answer that you get. And that’s what makes it so unique and so valuable because now you’re training the model to answer the questions the way you want them answered.”For CIOs, the shift isn’t just about tech enablement. It’s about integrating AI into enterprise architecture, aligning with business strategy, and managing the governance risks that come with scale. CIOs are becoming AI stewards — architecting not just systems, but trust and transformation.ConclusionIt’s only been a few years since AI emerged from its roots in academic computer science research, so it’s understandable that today’s enterprise organizations are, to a certain extent, feeling their way towards realizing AI’s potential.But a new playbook is emerging — one that helps CIOs access the value held in their data reserves, in business strategy, operational improvement, customer-facing experiences and a dozen more areas of the business.As a company that’s steeped in experience with clients large and small from all over the world, PwC is one of the leading choices that decision-makers turn to, to begin or rationalize and direct their existing AI journeys.Explore how PwC is helping CIOs embed AI into core operations, and see Rani’s latest insights at the June TechEx AI & Big Data Expo North America. #enables #shift #enablement #strategic #leadership

Tech AI robots help nurses beat burnout and transform hospital care Hospitals using AI-powered robots to support nurses, redefine patient care Published June 4, 2025 6:00am EDT close AI robots help nurses beat burnout and transform hospital care Artificial intelligence and robotics may help with nursing shortage. NEWYou can now listen to Fox News articles! The global healthcare system is expected to face a shortage of 4.5 million nurses by 2030, with burnout identified as a leading cause for this deficit. In response, Taiwan's hospitals are taking decisive action by integrating artificial intelligence and robotics to support their staff and maintain high standards of patient care. AI-powered NurabotNurabot: The AI nursing robot changing patient careNurabot, a collaborative nursing robot developed by Foxconn and Kawasaki Heavy Industries with Nvidia's AI technology, is designed to take on some of the most physically demanding and repetitive tasks in clinical care. These include delivering medications, transporting samples, patrolling wards and guiding visitors through hospital corridors. By handling these responsibilities, Nurabot allows nurses to focus on more meaningful aspects of patient care and helps reduce the physical fatigue that often leads to burnout. AI-powered NurabotUsing AI to build the hospitals of the futureFoxconn's approach to smart hospitals goes beyond deploying robots. The company has developed a suite of digital tools using Nvidia platforms, including AI models that monitor patient vitals and digital twins that simulate hospital environments for planning and training purposes.The process starts in the data center, where large AI models are trained on Nvidia supercomputers. Hospitals then use digital twins to test and train robots in virtual settings before deploying them in real-world scenarios, ensuring that these systems are both safe and effective.ARTIFICIAL INTELLIGENCE TRANSFORMS PATIENT CARE AND REDUCES BURNOUT, PHYSICIAN SAYS AI-powered NurabotAI robots in real hospitals: Results from Taiwan's Healthcare SystemTaichung Veterans General Hospital, along with other top hospitals in Taiwan, is at the forefront of this digital transformation. TCVGH has built digital twins of its wards and nursing stations, providing a virtual training ground for Nurabot before it is introduced to real hospital floors. According to Shu-Fang Liu, deputy director of the nursing department at TCVGH, robots like Nurabot are augmenting the capabilities of healthcare staff, enabling them to deliver more focused and meaningful care to patients. AI-powered NurabotWays Nurabot reduces nurse burnout and boosts efficiencyNurabot is already making a difference in daily hospital operations. The robot handles medicine deliveries, ward patrols and visitor guidance, which Foxconn estimates can reduce nurse workloads by up to 30%. In one ward, Nurabot delivers wound care kits and educational materials directly to patient bedsides, saving nurses multiple trips to supply rooms and allowing them to dedicate more time to their patients. The robot is also especially helpful during visiting hours and night shifts, when staffing levels are typically lower.Nurses hope future versions of Nurabot will be able to converse with patients in multiple languages, recognize faces for personalized interactions and even assist with lifting patients when needed. For example, a lung patient who needs two nurses to sit up for breathing exercises might only require one nurse with Nurabot's help, freeing the other to care for other patients. AI-powered NurabotKurt's key takeawaysWhen it comes to addressing the nursing shortage, Taiwan is demonstrating that AI and robotics can make a significant difference in hospitals. Instead of spending their shifts running errands or handling repetitive tasks, nurses now have robots like Nurabot to lend a hand. This means nurses can focus their energy on what matters most – caring for patients – while robots handle tasks such as delivering medication or guiding visitors around the hospital.It's a team effort between people and technology, and it's already helping healthcare staff provide better care for everyone.CLICK HERE TO GET THE FOX NEWS APPHow would you feel if a robot, not a human, delivered your medication during a hospital stay? Let us know by writing us at Cyberguy.com/Contact.For more of my tech tips and security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/Newsletter.Ask Kurt a question or let us know what stories you'd like us to cover.Follow Kurt on his social channels:Answers 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. #robots #help #nurses #beat #burnout

After detailing the VFX work on The Last of Us Season 1 in 2023, Alex Wang returns to reflect on how the scope and complexity have evolved in Season 2. With close to 30 years of experience in the visual effects industry, Fiona Campbell Westgate has contributed to major productions such as Ghost in the Shell, Avatar: The Way of Water, Ant-Man and the Wasp: Quantumania, and Nyad. Her work on Nyad earned her a VES Award for Outstanding Supporting Visual Effects in a Photoreal Feature. Collaboration with Craig Mazin and Neil Druckmann is key to shaping the visual universe of The Last of Us. Can you share with us how you work with them and how they influence the visual direction of the series? Alex Wang // Craig visualizes the shot or scene before putting words on the page. His writing is always exceptionally detailed and descriptive, ultimately helping us to imagine the shot. Of course, no one understands The Last of Us better than Neil, who knows all aspects of the lore very well. He’s done much research and design work with the Naughty Dog team, so he gives us good guidance regarding creature and environment designs. I always try to begin with concept art to get the ball rolling with Craig and Neil’s ideas. This season, we collaborated with Chromatic Studios for concept art. They also contributed to the games, so I felt that continuity was beneficial for our show. Fiona Campbell Westgate // From the outset, it was clear that collaborating with Craig would be an exceptional experience. Early meetings revealed just how personable and invested Craig is. He works closely with every department to ensure that each episode is done to the highest level. Craig places unwavering trust in our VFX Supervisor, Alex Wang. They have an understanding between them that lends to an exceptional partnership. As the VFX Producer, I know how vital the dynamic between the Showrunner and VFX Supervisor is; working with these two has made for one of the best professional experiences of my career.  Photograph by Liane Hentscher/HBO How has your collaboration with Craig evolved between the first and second seasons? Were there any adjustments in the visual approach or narrative techniques you made this season? Alex Wang // Since everything was new in Season 1, we dedicated a lot of time and effort to exploring the show’s visual language, and we all learned a great deal about what worked and what didn’t for the show. In my initial conversations with Craig about Season 2, it was clear that he wanted to expand the show’s scope by utilizing what we established and learned in Season 1. He felt significantly more at ease fully committing to using VFX to help tell the story this season. The first season involved multiple VFX studios to handle the complexity of the effects. How did you divide the work among different studios for the second season? Alex Wang // Most of the vendors this season were also in Season 1, so we already had a shorthand. The VFX Producer, Fiona Campbell Westgate, and I work closely together to decide how to divide the work among our vendors. The type of work needs to be well-suited for the vendor and fit into our budget and schedule. We were extremely fortunate to have the vendors we did this season. I want to take this opportunity to thank Weta FX, DNEG, RISE, Distillery VFX, Storm Studios, Important Looking Pirates, Blackbird, Wylie Co., RVX, and VDK. We also had ILM for concept art and Digital Domain for previs. Fiona Campbell Westgate // Alex Wang and I were very aware of the tight delivery schedule, which added to the challenge of distributing the workload. We planned the work based on the individual studio’s capabilities, and tried not to burden them with back to back episodes wherever possible. Fortunately, there was shorthand with vendors from Season One, who were well-acquainted with the process and the quality of work the show required. The town of Jackson is a key location in The Last of Us. Could you explain how you approached creating and expanding this environment for the second season? Alex Wang // Since Season 1, this show has created incredible sets. However, the Jackson town set build is by far the most impressive in terms of scope. They constructed an 822 ft x 400 ft set in Minaty Bay that resembled a real town! I had early discussions with Production Designer Don MacAulay and his team about where they should concentrate their efforts and where VFX would make the most sense to take over. They focused on developing the town’s main street, where we believed most scenes would occur. There is a big reveal of Jackson in the first episode after Ellie comes out of the barn. Distillery VFX was responsible for the town’s extension, which appears seamless because the team took great pride in researching and ensuring the architecture aligned with the set while staying true to the tone of Jackson, Wyoming. Fiona Campbell Westgate // An impressive set was constructed in Minaty Bay, which served as the foundation for VFX to build upon. There is a beautiful establishing shot of Jackson in Episode 1 that was completed by Distillery, showing a safe and almost normal setting as Season Two starts. Across the episodes, Jackson set extensions were completed by our partners at RISE and Weta. Each had a different phase of Jackson to create, from almost idyllic to a town immersed in Battle.  What challenges did you face filming Jackson on both real and virtual sets? Was there a particular fusion between visual effects and live-action shots to make it feel realistic? Alex Wang // I always advocate for building exterior sets outdoors to take advantage of natural light. However, the drawback is that we cannot control the weather and lighting when filming over several days across two units. In Episode 2, there’s supposed to be a winter storm in Jackson, so maintaining consistency within the episode was essential. On sunny and rainy days, we used cranes to lift large 30x60ft screens to block the sun or rain. It was impossible to shield the entire set from the rain or sun, so we prioritized protecting the actors from sunlight or rain. Thus, you can imagine there was extensive weather cleanup for the episode to ensure consistency within the sequences. Fiona Campbell Westgate // We were fortunate that production built a large scale Jackson set. It provided a base for the full CG Jackson aerial shots and CG Set Extensions. The weather conditions at Minaty Bay presented a challenge during the filming of the end of the Battle sequence in Episode 2. While there were periods of bright sunshine, rainfall occurred during the filming of the end of the Battle sequence in Episode 2. In addition to the obvious visual effects work, it became necessary to replace the ground cover. Photograph by Liane Hentscher/HBO The attack on Jackson by the horde of infected in season 2 is a very intense moment. How did you approach the visual effects for this sequence? What techniques did you use to make the scale of the attack feel as impressive as it did? Alex Wang // We knew this would be a very complex sequence to shoot, and for it to be successful, we needed to start planning with the HODs from the very beginning. We began previs during prep with Weta FX and the episode’s director, Mark Mylod. The previs helped us understand Mark and the showrunner’s vision. This then served as a blueprint for all departments to follow, and in many instances, we filmed the previs. Fiona Campbell Westgate // The sheer size of the CG Infected Horde sets the tone for the scale of the Battle. It’s an intimidating moment when they are revealed through the blowing snow. The addition of CG explosions and atmospheric effects contributed in adding scale to the sequence.  Can you give us an insight into the technical challenges of capturing the infected horde? How much of the effect was done using CGI, and how much was achieved with practical effects? Alex Wang // Starting with a detailed previs that Mark and Craig approved was essential for planning the horde. We understood that we would never have enough stunt performers to fill a horde, nor could they carry out some stunts that would be too dangerous. I reviewed the previs with Stunt Coordinator Marny Eng numerous times to decide the best placements for her team’s stunt performers. We also collaborated with Barrie Gower from the Prosthetics team to determine the most effective allocation of his team’s efforts. Stunt performers positioned closest to the camera would receive the full prosthetic treatment, which can take hours. Weta FX was responsible for the incredible CG Infected horde work in the Jackson Battle. They have been a creative partner with HBO’s The Last of Us since Season 1, so they were brought on early for Season 2. I began discussions with Weta’s VFX supervisor, Nick Epstein, about how we could tackle these complex horde shots very early during the shoot. Typically, repetition in CG crowd scenes can be acceptable, such as armies with soldiers dressed in the same uniform or armour. However, for our Infected horde, Craig wanted to convey that the Infected didn’t come off an assembly line or all shop at the same clothing department store. Any repetition would feel artificial. These Infected were once civilians with families, or they were groups of raiders. We needed complex variations in height, body size, age, clothing, and hair. We built our base library of Infected, and then Nick and the Weta FX team developed a “mix and match” system, allowing the Infected to wear any costume and hair groom. A procedural texturing system was also developed for costumes, providing even greater variation. The most crucial aspect of the Infected horde was their motion. We had numerous shots cutting back-to-back with practical Infected, as well as shots where our CG Infected ran right alongside a stunt horde. It was incredibly unforgiving! Weta FX’s animation supervisor from Season 1, Dennis Yoo, returned for Season 2 to meet the challenge. Having been part of the first season, Dennis understood the expectations of Craig and Neil. Similar to issues of model repetition within a horde, it was relatively easy to perceive repetition, especially if they were running toward the same target. It was essential to enhance the details of their performances with nuances such as tripping and falling, getting back up, and trampling over each other. There also needed to be a difference in the Infected’s running speed. To ensure we had enough complexity within the horde, Dennis motion-captured almost 600 unique motion cycles. We had over a hundred shots in episode 2 that required CG Infected horde. Fiona Campbell Westgate // Nick Epstein, Weta VFX Supervisor, and Dennis Yoo, Weta Animation Supervisor, were faced with having to add hero, close-up Horde that had to integrate with practical Stunt performers. They achieved this through over 60 motion capture sessions and running it through a deformation system they developed. Every detail was applied to allow for a seamless blend with our practical Stunt performances. The Weta team created a custom costume and hair system that provided individual looks to the CG Infected Horde. We were able to avoid the repetitive look of a CG crowd due to these efforts. The movement of the infected horde is crucial for the intensity of the scene. How did you manage the animation and simulation of the infected to ensure smooth and realistic interaction with the environment? Fiona Campbell Westgate // We worked closely with the Stunt department to plan out positioning and where VFX would be adding the CG Horde. Craig Mazin wanted the Infected Horde to move in a way that humans cannot. The deformation system kept the body shape anatomically correct and allowed us to push the limits from how a human physically moves.  The Bloater makes a terrifying return this season. What were the key challenges in designing and animating this creature? How did you work on the Bloater’s interaction with the environment and other characters? Alex Wang // In Season 1, the Kansas City cul-de-sac sequence featured only a handful of Bloater shots. This season, however, nearly forty shots showcase the Bloater in broad daylight during the Battle of Jackson. We needed to redesign the Bloater asset to ensure it looked good in close-up shots from head to toe. Weta FX designed the Bloater for Season 1 and revamped the design for this season. Starting with the Bloater’s silhouette, it had to appear large, intimidating, and menacing. We explored enlarging the cordyceps head shape to make it feel almost like a crown, enhancing the Bloater’s impressive and strong presence. During filming, a stunt double stood in for the Bloater. This was mainly for scale reference and composition. It also helped the Infected stunt performers understand the Bloater’s spatial position, allowing them to avoid running through his space. Once we had an edit, Dennis mocapped the Bloater’s performances with his team. It is always challenging to get the motion right for a creature that weighs 600 pounds. We don’t want the mocap to be overly exaggerated, but it does break the character if the Bloater feels too “light.” The brilliant animation team at Weta FX brought the Bloater character to life and nailed it! When Tommy goes head-to-head with the Bloater, Craig was quite specific during the prep days about how the Bloater would bubble, melt, and burn as Tommy torches him with the flamethrower. Important Looking Pirates took on the “Burning Bloater” sequence, led by VFX Supervisor Philip Engstrom. They began with extensive R&D to ensure the Bloater’s skin would start to bubble and burn. ILP took the final Bloater asset from Weta FX and had to resculpt and texture the asset for the Bloater’s final burn state. Craig felt it was important for the Bloater to appear maimed at the end. The layers of FX were so complex that the R&D continued almost to the end of the delivery schedule. Fiona Campbell Westgate // This season the Bloater had to be bigger, more intimidating. The CG Asset was recreated to withstand the scrutiny of close ups and in daylight. Both Craig Mazin and Neil Druckmann worked closely with us during the process of the build. We referenced the game and applied elements of that version with ours. You’ll notice that his head is in the shape of crown, this is to convey he’s a powerful force.  During the Burning Bloater sequence in Episode 2, we brainstormed with Philip Engström, ILP VFX Supervisor, on how this creature would react to the flamethrower and how it would affect the ground as it burns. When the Bloater finally falls to the ground and dies, the extraordinary detail of the embers burning, fluid draining and melting the surrounding snow really sells that the CG creature was in the terrain.  Given the Bloater’s imposing size, how did you approach its integration into scenes with the actors? What techniques did you use to create such a realistic and menacing appearance? Fiona Campbell Westgate // For the Bloater, a stunt performer wearing a motion capture suit was filmed on set. This provided interaction with the actors and the environment. VFX enhanced the intensity of his movements, incorporating simulations to the CG Bloater’s skin and muscles that would reflect the weight and force as this terrifying creature moves.  Seattle in The Last of Us is a completely devastated city. Can you talk about how you recreated this destruction? What were the most difficult visual aspects to realize for this post-apocalyptic city? Fiona Campbell Westgate // We were meticulous in blending the CG destruction with the practical environment. The flora’s ability to overtake the environment had to be believable, and we adhered to the principle of form follows function. Due to the vastness of the CG devastation it was crucial to avoid repetitive effects. Consequently, our vendors were tasked with creating bespoke designs that evoked a sense of awe and beauty. Was Seattle’s architecture a key element in how you designed the visual effects? How did you adapt the city’s real-life urban landscape to meet the needs of the story while maintaining a coherent aesthetic? Alex Wang // It’s always important to Craig and Neil that we remain true to the cities our characters are in. DNEG was one of our primary vendors for Boston in Season 1, so it was natural for them to return for Season 2, this time focusing on Seattle. DNEG’s VFX Supervisor, Stephen James, who played a crucial role in developing the visual language of Boston for Season 1, also returns for this season. Stephen and Melaina Maceled a team to Seattle to shoot plates and perform lidar scans of parts of the city. We identified the buildings unique to Seattle that would have existed in 2003, so we ensured these buildings were always included in our establishing shots. Overgrowth and destruction have significantly influenced the environments in The Last of Us. The environment functions almost as a character in both Season 1 and Season 2. In the last season, the building destruction in Boston was primarily caused by military bombings. During this season, destruction mainly arises from dilapidation. Living in the Pacific Northwest, I understand how damp it can get for most of the year. I imagined that, over 20 years, the integrity of the buildings would be compromised by natural forces. This abundant moisture creates an exceptionally lush and vibrant landscape for much of the year. Therefore, when designing Seattle, we ensured that the destruction and overgrowth appeared intentional and aesthetically distinct from those of Boston. Fiona Campbell Westgate // Led by Stephen James, DNEG VFX Supervisor, and Melaina Mace, DNEG DFX Supervisor, the team captured photography, drone footage and the Clear Angle team captured LiDAR data over a three-day period in Seattle. It was crucial to include recognizable Seattle landmarks that would resonate with people familiar with the game.  The devastated city almost becomes a character in itself this season. What aspects of the visual effects did you have to enhance to increase the immersion of the viewer into this hostile and deteriorated environment? Fiona Campbell Westgate // It is indeed a character. Craig wanted it to be deteriorated but to have moments where it’s also beautiful in its devastation. For instance, in the Music Store in Episode 4 where Ellie is playing guitar for Dina, the deteriorated interior provides a beautiful backdrop to this intimate moment. The Set Decorating team dressed a specific section of the set, while VFX extended the destruction and overgrowth to encompass the entire environment, immersing the viewer in strange yet familiar surroundings. Photograph by Liane Hentscher/HBO The sequence where Ellie navigates a boat through a violent storm is stunning. What were the key challenges in creating this scene, especially with water simulation and the storm’s effects? Alex Wang // In the concluding episode of Season 2, Ellie is deep in Seattle, searching for Abby. The episode draws us closer to the Aquarium, where this area of Seattle is heavily flooded. Naturally, this brings challenges with CG water. In the scene where Ellie encounters Isaac and the W.L.F soldiers by the dock, we had a complex shoot involving multiple locations, including a water tank and a boat gimbal. There were also several full CG shots. For Isaac’s riverine boat, which was in a stormy ocean, I felt it was essential that the boat and the actors were given the appropriate motion. Weta FX assisted with tech-vis for all the boat gimbal work. We began with different ocean wave sizes caused by the storm, and once the filmmakers selected one, the boat’s motion in the tech-vis fed the special FX gimbal. When Ellie gets into the Jon boat, I didn’t want it on the same gimbal because I felt it would be too mechanical. Ellie’s weight needed to affect the boat as she got in, and that wouldn’t have happened with a mechanical gimbal. So, we opted to have her boat in a water tank for this scene. Special FX had wave makers that provided the boat with the appropriate movement. Instead of guessing what the ocean sim for the riverine boat should be, the tech- vis data enabled DNEG to get a head start on the water simulations in post-production. Craig wanted this sequence to appear convincingly dark, much like it looks out on the ocean at night. This allowed us to create dramatic visuals, using lightning strikes at moments to reveal depth. Were there any memorable moments or scenes from the series that you found particularly rewarding or challenging to work on from a visual effects standpoint? Alex Wang // The Last of Us tells the story of our characters’ journey. If you look at how season 2 begins in Jackson, it differs significantly from how we conclude the season in Seattle. We seldom return to the exact location in each episode, meaning every episode presents a unique challenge. The scope of work this season has been incredibly rewarding. We burned a Bloater, and we also introduced spores this season! Photograph by Liane Hentscher/HBO Looking back on the project, what aspects of the visual effects are you most proud of? Alex Wang // The Jackson Battle was incredibly complex, involving a grueling and lengthy shoot in quite challenging conditions, along with over 600 VFX shots in episode 2. It was truly inspiring to witness the determination of every department and vendor to give their all and create something remarkable. Fiona Campbell Westgate // I am immensely proud of the exceptional work accomplished by all of our vendors. During the VFX reviews, I found myself clapping with delight when the final shots were displayed; it was exciting to see remarkable results of the artists’ efforts come to light.  How long have you worked on this show? Alex Wang // I’ve been on this season for nearly two years. Fiona Campbell Westgate // A little over one year; I joined the show in April 2024. What’s the VFX shots count? Alex Wang // We had just over 2,500 shots this Season. Fiona Campbell Westgate // In Season 2, there were a total of 2656 visual effects shots. What is your next project? Fiona Campbell Westgate // Stay tuned… A big thanks for your time. WANT TO KNOW MORE?Blackbird: Dedicated page about The Last of Us – Season 2 website.DNEG: Dedicated page about The Last of Us – Season 2 on DNEG website.Important Looking Pirates: Dedicated page about The Last of Us – Season 2 website.RISE: Dedicated page about The Last of Us – Season 2 website.Weta FX: Dedicated page about The Last of Us – Season 2 website. © Vincent Frei – The Art of VFX – 2025 #last #season #alex #wang #production