Warning! We're about to go into deep endgame spoilers for Blue Prince, well beyond rolling the credits by reaching Room 46. Read on at your own risk.I had been playing Blue Prince for more than 100 hours before I felt like I truly understood what the game was really about.The revelation came in the form of a journal entry, secreted away in a safety deposit box, hidden within the sometimes tough-to-access vault of the strange and shifting Mount Holly Manor. Reaching the paper requires solving one of Blue Prince's toughest, most obtuse, and most rewarding puzzles, one you won't even realize exists until you've broken through riddle after riddle and uncovered mystery after mystery. It recontextualizes everything that has come before it, not only the winding and involved test of wits that is the manor itself, but the story that had to be similarly excavated along the way--one of political intrigue and family tragedy, the rising and falling of kingdoms, the stoking of revolution, and the sacrifice necessary to breathe life into ideals.Continue Reading at GameSpot

At GTC Paris — held alongside VivaTech, Europe’s largest tech event — NVIDIA founder and CEO Jensen Huang delivered a clear message: Europe isn’t just adopting AI — it’s building it. “We now have a new industry, an AI industry, and it’s now part of the new infrastructure, called intelligence infrastructure, that will be used by every country, every society,” Huang said, addressing an audience gathered online and at the iconic Dôme de Paris. From exponential inference growth to quantum breakthroughs, and from infrastructure to industry, agentic AI to robotics, Huang outlined how the region is laying the groundwork for an AI-powered future. A New Industrial Revolution At the heart of this transformation, Huang explained, are systems like GB200 NVL72 — “one giant GPU” and NVIDIA’s most powerful AI platform yet — now in full production and powering everything from sovereign models to quantum computing. “This machine was designed to be a thinking machine, a thinking machine, in the sense that it reasons, it plans, it spends a lot of time talking to itself,” Huang said, walking the audience through the size and scale of these machines and their performance. At GTC Paris, Huang showed audience members the innards of some of NVIDIA’s latest hardware. There’s more coming, with Huang saying NVIDIA’s partners are now producing 1,000 GB200 systems a week, “and this is just the beginning.” He walked the audience through a range of available systems ranging from the tiny NVIDIA DGX Spark to rack-mounted RTX PRO Servers. Huang explained that NVIDIA is working to help countries use technologies like these to build both AI infrastructure — services built for third parties to use and innovate on — and AI factories, which companies build for their own use, to generate revenue. NVIDIA is partnering with European governments, telcos and cloud providers to deploy NVIDIA technologies across the region. NVIDIA is also expanding its network of technology centers across Europe — including new hubs in Finland, Germany, Spain, Italy and the U.K. — to accelerate skills development and quantum growth. Quantum Meets Classical Europe’s quantum ambitions just got a boost. The NVIDIA CUDA-Q platform is live on Denmark’s Gefion supercomputer, opening new possibilities for hybrid AI and quantum engineering. In addition, Huang announced that CUDA-Q is now available on NVIDIA Grace Blackwell systems. Across the continent, NVIDIA is partnering with supercomputing centers and quantum hardware builders to advance hybrid quantum-AI research and accelerate quantum error correction. “Quantum computing is reaching an inflection point,” Huang said. “We are within reach of being able to apply quantum computing, quantum classical computing, in areas that can solve some interesting problems in the coming years.” Sovereign Models, Smarter Agents European developers want more control over their models. Enter NVIDIA Nemotron, designed to help build large language models tuned to local needs. “And so now you know that you have access to an enhanced open model that is still open, that is top of the leader chart,” Huang said. These models will be coming to Perplexity, a reasoning search engine, enabling secure, multilingual AI deployment across Europe. “You can now ask and get questions answered in the language, in the culture, in the sensibility of your country,” Huang said. Huang explained how NVIDIA is helping countries across Europe build AI infrastructure. Every company will build its own agents, Huang said. To help create those agents, Huang introduced a suite of agentic AI blueprints, including an Agentic AI Safety blueprint for enterprises and governments. The new NVIDIA NeMo Agent toolkit and NVIDIA AI Blueprint for building data flywheels further accelerate the development of safe, high-performing AI agents. To help deploy these agents, NVIDIA is partnering with European governments, telcos and cloud providers to deploy the DGX Cloud Lepton platform across the region, providing instant access to accelerated computing capacity. “One model architecture, one deployment, and you can run it anywhere,” Huang said, adding that Lepton is now integrated with Hugging Face, giving developers direct access to global compute. The Industrial Cloud Goes Live AI isn’t just virtual. It’s powering physical systems, too, sparking a new industrial revolution. “We’re working on industrial AI with one company after another,” Huang said, describing work to build digital twins based on the NVIDIA Omniverse platform with companies across the continent. Huang explained that everything he showed during his keynote was “computer simulation, not animation” and that it looks beautiful because “it turns out the world is beautiful, and it turns out math is beautiful.” To further this work, Huang announced NVIDIA is launching the world’s first industrial AI cloud — to be built in Germany — to help Europe’s manufacturers simulate, automate and optimize at scale. “Soon, everything that moves will be robotic,” Huang said. “And the car is the next one.” NVIDIA DRIVE, NVIDIA’s full-stack AV platform, is now in production to accelerate the large-scale deployment of safe, intelligent transportation. And to show what’s coming next, Huang was joined on stage by Grek, a pint-sized robot, as Huang talked about how NVIDIA partnered with DeepMind and Disney to build Newton, the world’s most advanced physics training engine for robotics. The Next Wave The next wave of AI has begun — and it’s exponential, Huang explained. “We have physical robots, and we have information robots. We call them agents,” Huang said. “The technology necessary to teach a robot to manipulate, to simulate — and of course, the manifestation of an incredible robot — is now right in front of us.” This new era of AI is being driven by a surge in inference workloads. “The number of people using inference has gone from 8 million to 800 million — 100x in just a couple of years,” Huang said. To meet this demand, Huang emphasized the need for a new kind of computer: “We need a special computer designed for thinking, designed for reasoning. And that’s what Blackwell is — a thinking machine.” Huang and Grek, as he explained how AI is driving advancements in robotics. These Blackwell-powered systems will live in a new class of data centers — AI factories — built to generate tokens, the raw material of modern intelligence. “These AI factories are going to generate tokens,” Huang said, turning to Grek with a smile. “And these tokens are going to become your food, little Grek.” With that, the keynote closed on a bold vision: a future powered by sovereign infrastructure, agentic AI, robotics — and exponential inference — all built in partnership with Europe. Watch the NVIDIA GTC Paris keynote from Huang at VivaTech and explore GTC Paris sessions.

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Take the Speakers, Headphones, and Earphones SurveyTake other PCMag surveys. Each completed survey is a chance to win a $250 Amazon gift card. OFFICIAL SWEEPSTAKES RULESNO PURCHASE NECESSARY TO ENTER OR WIN. A PURCHASE WILL NOT INCREASE YOUR CHANCES OF WINNING. VOID WHERE PROHIBITED. Readers' Choice Sweepstakes (the "Sweepstakes") is governed by these official rules (the "Sweepstakes Rules"). The Sweepstakes begins on May 9, 2025, at 12:00 AM ET and ends on July 27, 2025, at 11:59 PM ET (the "Sweepstakes Period").SPONSOR: Ziff Davis, LLC, with an address of 360 Park Ave South, Floor 17, New York, NY 10010 (the "Sponsor").ELIGIBILITY: This Sweepstakes is open to individuals who are eighteen (18) years of age or older at the time of entry who are legal residents of the fifty (50) United States of America or the District of Columbia. 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Monitoring and Support EngineerKeyword StudiosPasig City Metro Manila Philippines2 hours agoApplyWe are seeking an experienced Monitoring and Support Engineer to support the technology initiatives of the IT Infrastructure team at Keywords. The Monitoring and Support Engineer will be responsible for follow-the-sun monitoring of IT infrastructure, prompt reaction on all infrastructure incident, primary resolution of infrastructure incidents and support requests.ResponsibilitiesFull scope of tasks including but not limited to:Ensure that all incidents are handled within SLAs.Initial troubleshooting of Infrastructure incidents.Ensure maximum network & service availability through proactive monitoring.Ensure all the incident and alert tickets contain detailed technical information.Initial troubleshooting of Infrastructure incidents, restoration of services and escalation to level 3 experts if necessary.Participate in Problem management processes.Ensure that all incidents and critical alerts are documented and escalated if necessary.Ensure effective communication to customers about incidents and outages.Identify opportunities for process improvement and efficiency enhancements.Participate in documentation creation to reduce BAU support activities by ensuring that the Service Desks have adequate knowledge articles to close support tickets as level 1.Participate in reporting on monitored data and incidents on company infrastructure.Implement best practices and lessons learned from initiatives and projects to optimize future outcomes.RequirementsBachelor's degree in a relevant technical field or equivalent experience.Understanding of IT Infrastructure technologies, standards and trends.Technical background with 3+ years’ experience in IT operations role delivering IT infrastructure support, monitoring and incident management.Technical knowledge of the Microsoft Stack, Windows networking, Active Directory, ExchangeTechnical knowledge of Network, Storage and Server equipment, virtualization and production setupsExceptional communication and presentation skills, with the ability to articulate technical concepts to non-technical audiences.Strong analytical and problem-solving skills.Strong customer service orientation.BenefitsGreat Place to Work certified for 4 consecutive yearsFlexible work arrangementGlobal exposure Create Your Profile — Game companies can contact you with their relevant job openings. Apply

Enterprise artificial intelligence investment is unprecedented, with IDC projecting global spending on AI and GenAI to double to $631 billion by 2028. Yet beneath the impressive budget allocations and boardroom enthusiasm lies a troubling reality: most organisations struggle to translate their AI ambitions into operational success.The sobering statistics behind AI’s promiseModelOp’s 2025 AI Governance Benchmark Report, based on input from 100 senior AI and data leaders at Fortune 500 enterprises, reveals a disconnect between aspiration and execution.While more than 80% of enterprises have 51 or more generative AI projects in proposal phases, only 18% have successfully deployed more than 20 models into production.The execution gap represents one of the most significant challenges facing enterprise AI today. Most generative AI projects still require 6 to 18 months to go live – if they reach production at all.The result is delayed returns on investment, frustrated stakeholders, and diminished confidence in AI initiatives in the enterprise.The cause: Structural, not technical barriersThe biggest obstacles preventing AI scalability aren’t technical limitations – they’re structural inefficiencies plaguing enterprise operations. The ModelOp benchmark report identifies several problems that create what experts call a “time-to-market quagmire.”Fragmented systems plague implementation. 58% of organisations cite fragmented systems as the top obstacle to adopting governance platforms. Fragmentation creates silos where different departments use incompatible tools and processes, making it nearly impossible to maintain consistent oversight in AI initiatives.Manual processes dominate despite digital transformation. 55% of enterprises still rely on manual processes – including spreadsheets and email – to manage AI use case intake. The reliance on antiquated methods creates bottlenecks, increases the likelihood of errors, and makes it difficult to scale AI operations.Lack of standardisation hampers progress. Only 23% of organisations implement standardised intake, development, and model management processes. Without these elements, each AI project becomes a unique challenge requiring custom solutions and extensive coordination by multiple teams.Enterprise-level oversight remains rare Just 14% of companies perform AI assurance at the enterprise level, increasing the risk of duplicated efforts and inconsistent oversight. The lack of centralised governance means organisations often discover they’re solving the same problems multiple times in different departments.The governance revolution: From obstacle to acceleratorA change is taking place in how enterprises view AI governance. Rather than seeing it as a compliance burden that slows innovation, forward-thinking organisations recognise governance as an important enabler of scale and speed.Leadership alignment signals strategic shift. The ModelOp benchmark data reveals a change in organisational structure: 46% of companies now assign accountability for AI governance to a Chief Innovation Officer – more than four times the number who place accountability under Legal or Compliance. This strategic repositioning reflects a new understanding that governance isn’t solely about risk management, but can enable innovation.Investment follows strategic priority. A financial commitment to AI governance underscores its importance. According to the report, 36% of enterprises have budgeted at least $1 million annually for AI governance software, while 54% have allocated resources specifically for AI Portfolio Intelligence to track value and ROI.What high-performing organisations do differentlyThe enterprises that successfully bridge the ‘execution gap’ share several characteristics in their approach to AI implementation:Standardised processes from day one. Leading organisations implement standardised intake, development, and model review processes in AI initiatives. Consistency eliminates the need to reinvent workflows for each project and ensures that all stakeholders understand their responsibilities.Centralised documentation and inventory. Rather than allowing AI assets to proliferate in disconnected systems, successful enterprises maintain centralised inventories that provide visibility into every model’s status, performance, and compliance posture.Automated governance checkpoints. High-performing organisations embed automated governance checkpoints throughout the AI lifecycle, helping ensure compliance requirements and risk assessments are addressed systematically rather than as afterthoughts.End-to-end traceability. Leading enterprises maintain complete traceability of their AI models, including data sources, training methods, validation results, and performance metrics.Measurable impact of structured governanceThe benefits of implementing comprehensive AI governance extend beyond compliance. Organisations that adopt lifecycle automation platforms reportedly see dramatic improvements in operational efficiency and business outcomes.A financial services firm profiled in the ModelOp report experienced a halving of time to production and an 80% reduction in issue resolution time after implementing automated governance processes. Such improvements translate directly into faster time-to-value and increased confidence among business stakeholders.Enterprises with robust governance frameworks report the ability to many times more models simultaneously while maintaining oversight and control. This scalability lets organisations pursue AI initiatives in multiple business units without overwhelming their operational capabilities.The path forward: From stuck to scaledThe message from industry leaders that the gap between AI ambition and execution is solvable, but it requires a shift in approach. Rather than treating governance as a necessary evil, enterprises should realise it enables AI innovation at scale.Immediate action items for AI leadersOrganisations looking to escape the ‘time-to-market quagmire’ should prioritise the following:Audit current state: Conduct an assessment of existing AI initiatives, identifying fragmented processes and manual bottlenecksStandardise workflows: Implement consistent processes for AI use case intake, development, and deployment in all business unitsInvest in integration: Deploy platforms to unify disparate tools and systems under a single governance frameworkEstablish enterprise oversight: Create centralised visibility into all AI initiatives with real-time monitoring and reporting abilitiesThe competitive advantage of getting it rightOrganisations that can solve the execution challenge will be able to bring AI solutions to market faster, scale more efficiently, and maintain the trust of stakeholders and regulators.Enterprises that continue with fragmented processes and manual workflows will find themselves disadvantaged compared to their more organised competitors. Operational excellence isn’t about efficiency but survival.The data shows enterprise AI investment will continue to grow. Therefore, the question isn’t whether organisations will invest in AI, but whether they’ll develop the operational abilities necessary to realise return on investment. The opportunity to lead in the AI-driven economy has never been greater for those willing to embrace governance as an enabler not an obstacle.(Image source: Unsplash)

Perhaps the oldest, most pernicious form of human bias is that of men toward women. It often started at the moment of birth. In ancient Athens, at a public ceremony called the amphidromia, fathers would inspect a newborn and decide whether it would be part of the family, or be cast away. One often socially acceptable reason for abandoning the baby: It was a girl. Female infanticide has been distressingly common in many societies — and its practice is not just ancient history. In 1990, the Nobel Prize-winning economist Amartya Sen looked at birth ratios in Asia, North Africa, and China and calculated that more than 100 million women were essentially “missing” — meaning that, based on the normal ratio of boys to girls at birth and the longevity of both genders, there was a huge missing number of girls who should have been born, but weren’t. Sen’s estimate came before the truly widespread adoption of ultrasound tests that could determine the sex of a fetus in utero — which actually made the problem worse, leading to a wave of sex-selective abortions. These were especially common in countries like India and China; the latter’s one-child policy and old biases made families desperate for their one child to be a boy. The Economist has estimated that since 1980 alone, there have been approximately 50 million fewer girls born worldwide than would naturally be expected, which almost certainly means that roughly that nearly all of those girls were aborted for no other reason than their sex. The preference for boys was a bias that killed in mass numbers.But in one of the most important social shifts of our time, that bias is changing. In a great cover story earlier this month, The Economist reported that the number of annual excess male births has fallen from a peak of 1.7 million in 2000 to around 200,000, which puts it back within the biologically standard birth ratio of 105 boys for every 100 girls. Countries that once had highly skewed sex ratios — like South Korea, which saw almost 116 boys born for every 100 girls in 1990 — now have normal or near-normal ratios. Altogether, The Economist estimated that the decline in sex preference at birth in the past 25 years has saved the equivalent of 7 million girls. That’s comparable to the number of lives saved by anti-smoking efforts in the US. So how, exactly, have we overcome a prejudice that seemed so embedded in human society?Success in school and the workplaceFor one, we have relaxed discrimination against girls and women in other ways — in school and in the workplace. With fewer limits, girls are outperforming boys in the classroom. In the most recent international PISA tests, considered the gold standard for evaluating student performance around the world, 15-year-old girls beat their male counterparts in reading in 79 out of 81 participating countries or economies, while the historic male advantage in math scores has fallen to single digits. Girls are also dominating in higher education, with 113 female students at that level for every 100 male students. While women continue to earn less than men, the gender pay gap has been shrinking, and in a number of urban areas in the US, young women have actually been outearning young men. Government policies have helped accelerate that shift, in part because they have come to recognize the serious social problems that eventually result from decades of anti-girl discrimination. In countries like South Korea and China, which have long had some of the most skewed gender ratios at birth, governments have cracked down on technologies that enable sex-selective abortion. In India, where female infanticide and neglect have been particularly horrific, slogans like “Save the Daughter, Educate the Daughter” have helped change opinions. A changing preferenceThe shift is being seen not just in birth sex ratios, but in opinion polls — and in the actions of would-be parents.Between 1983 and 2003, The Economist reported, the proportion of South Korean women who said it was “necessary” to have a son fell from 48 percent to 6 percent, while nearly half of women now say they want daughters. In Japan, the shift has gone even further — as far back as 2002, 75 percent of couples who wanted only one child said they hoped for a daughter.In the US, which allows sex selection for couples doing in-vitro fertilization, there is growing evidence that would-be parents prefer girls, as do potential adoptive parents. While in the past, parents who had a girl first were more likely to keep trying to have children in an effort to have a boy, the opposite is now true — couples who have a girl first are less likely to keep trying. A more equal futureThere’s still more progress to be made. In northwest of India, for instance, birth ratios that overly skew toward boys are still the norm. In regions of sub-Saharan Africa, birth sex ratios may be relatively normal, but post-birth discrimination in the form of poorer nutrition and worse medical care still lingers. And course, women around the world are still subject to unacceptable levels of violence and discrimination from men.And some of the reasons for this shift may not be as high-minded as we’d like to think. Boys around the world are struggling in the modern era. They increasingly underperform in education, are more likely to be involved in violent crime, and in general, are failing to launch into adulthood. In the US, 20 percent of American men between 25 and 34 still live with their parents, compared to 15 percent of similarly aged women. It also seems to be the case that at least some of the increasing preference for girls is rooted in sexist stereotypes. Parents around the world may now prefer girls partly because they see them as more likely to take care of them in their old age — meaning a different kind of bias against women, that they are more natural caretakers, may be paradoxically driving the decline in prejudice against girls at birth.But make no mistake — the decline of boy preference is a clear mark of social progress, one measured in millions of girls’ lives saved. And maybe one Father’s Day, not too long from now, we’ll reach the point where daughters and sons are simply children: equally loved and equally welcomed.A version of this story originally appeared in the Good News newsletter. Sign up here!See More: