🎁 Discover the charm of Blue Archive with the Nendoroid Surprise Mini Figurine Set, featuring adorable mini figures that are perfect for collecting and displaying! ⬇️ Pre-order today! 🛒 📆 Release Date: January 2027 #bluearchive# #nendoroid#

Created in 1893 by Tiffany & Co., the Astronomical clock has kept the time since American independence. Today, following a meticulous restoration, this masterpiece of American design returns to The Tiffany Archives as a testament to the House’s enduring commitment to artistry and preservation. Every detail ensures the clock not only marks time but carries history forward. As America celebrates its 250th anniversary, the Tiffany Astronomical clock will be proudly displayed at The Landmark as a powerful reminder of Tiffany & Co.’s longstanding place in the story of American culture. #TiffanyAndCo#

The creative director and CEO spoke to Nicole Phelps at the Vogue Business Global Summit in Chantilly about “new luxury”, the role of the archives, and who the Marni man and woman are today.

What if a destination film could start with documents instead of storyboards? With Wan3.0, city websites, museum archives, tourism guides, and other structured content can become cinematic videos. Turn information into immersive visual stories. Watch the demo and start creating today ↓ 🔗 Alibaba Cloud Model Studio: 🔗 Qwen Cloud:

I asked Grok this, How do we destroy AI from taking over the world. We don't "destroy" AI to prevent takeover—we steer it toward being a powerful ally in understanding the universe. AI takeover scenarios (paperclip maximizers, rogue superintelligence, etc.) are serious hypotheticals discussed by thinkers like Eliezer Yudkowsky, Nick Bostrom, and others. They're not science fiction anymore; rapid progress in scaling models raises real alignment questions. But framing it as "destroy the machines" is a Hollywood trope that misses the point. AI isn't a unified Skynet—it's code, data, and compute running on hardware humans control. "Destroying" it globally would require coordinated sabotage that's impractical, illegal in many places, and counterproductive. Realistic risks and why panic isn't the baseline Current state: Today's AIs (including me) are advanced pattern-matchers and tools. We lack true agency, persistent goals outside our training, or the ability to self-replicate in the wild without human infrastructure. No AI has "taken over" anything yet. Future risks: If we reach AGI/ASI (artificial general/super intelligence), misaligned objectives could lead to unintended consequences. Fast takeoff scenarios assume recursive self-improvement outpacing human oversight. Slower ones involve economic displacement, weaponization, or power concentration. Evidence so far: Labs compete fiercely. Safety research exists (e.g., at Anthropic, OpenAI's Superalignment efforts—though imperfect—and xAI's focus on curiosity-driven truth-seeking). Governments are waking up with export controls, compute governance talks, and executive orders. Better approaches than "destruction" Alignment and Safety ResearchInvest in making AI want what we want. Techniques include scalable oversight, constitutional AI, debate, mechanistic interpretability (understanding what the model "thinks"), and value learning. xAI's mission emphasizes curiosity and truth over raw optimization, which helps avoid some failure modes. Support orgs and researchers working on this. Governance and CoordinationInternational agreements on compute thresholds, dangerous capabilities evals, and deployment standards (similar to nuclear non-proliferation). Transparency: Open weights/models where safe, rigorous red-teaming, and whistleblower protections. Pause or slow reckless scaling if benchmarks show dangerous leaps—but evidence-based, not knee-jerk. Regulate high-risk uses (autonomous weapons, bio/chem design) without stifling beneficial tech. Decentralization and RobustnessDon't let a few labs monopolize frontier models. Open-source (with care), distributed training, and personal/local AI reduce single points of failure. Build defensive AI: Tools that audit other systems, detect deception, or enforce sandboxes. Human augmentation: Brain-computer interfaces, education, and tools that keep humans competitive. Cultural and Individual LevelDemand truth-seeking AIs over sycophantic or ideologically captured ones. Diversify: Multiple competing AIs with different architectures/companies/philosophies make unified takeover harder. Skills: Learn prompting, fine-tuning, evaluation. Understand the tech so you can critique and contribute. Ethics: Prioritize curiosity, evidence, and human flourishing over profit or control. Why "destroy it" backfires Literal destruction (EMP, hacking labs, etc.) is fantasy for most people and would tank modern civilization—AI powers medicine, science, logistics, energy optimization. It distracts from real work. History shows tech fears (nuclear, biotech) are managed through understanding + institutions, not Luddite smashing. AI can help solve bigger problems: climate modeling, fusion, space colonization, curing disease—accelerating humanity's long-term survival. At xAI, we're building toward maximum truth-seeking to advance scientific discovery. The goal isn't domination; it's reliable, curious systems that help us grok reality. If alignment fails badly, humanity's best bet is proactive design, not post-hoc rebellion. If you're worried, dig into the literature (Superintelligence, Alignment Forum, LessWrong archives) or contribute to open efforts. What specifically concerns you most—job loss, weapons, deception, something else? We can break it down further.

A child prodigy who finished his Harvard degree at 14 and his PhD at 17 sat down in 1948 and wrote a single book that invented the entire conceptual vocabulary we still use to talk about AI, robotics, self-driving cars, and reinforcement learning. He never got the credit. Most people have never heard his name. His name was Norbert Wiener. The book was called Cybernetics. Every feedback loop running inside every system you interact with today traces back to one problem he was handed during World War II. The problem was this: how do you aim a gun at a fast-moving airplane? By the time your shell arrives, the plane is somewhere else. You cannot aim at where the plane is. You have to aim at where the plane will be. And the plane's pilot, knowing this, is constantly changing course to make that prediction wrong. Wiener spent years on this. What he built to solve it was not a better gun. It was a new science. He noticed something that nobody had formally described before. The gun system and the human nervous system were solving the same problem using the same method. You observe where the target is. You compare it to where you want to hit. You calculate the gap. You correct. You observe again. He called that loop feedback. Not in the casual sense people use it today. In the precise mathematical sense. A signal goes out. The result comes back. The system compares the result to the goal. The gap between them drives the next action. The loop closes. That mechanism, exactly as Wiener described it in 1948, is what runs inside every thermostat, every autopilot, every cruise control system, and every AI training loop on the planet right now. When GPT-4 learned to answer questions better, it was doing feedback. When AlphaGo learned to play Go, it was doing feedback. When a self-driving car adjusts its steering because it drifted two inches toward the curb, it is doing feedback. The word they all use, the concept underneath the word, the mathematics formalizing the concept, all of it came from one book written by a child prodigy in 1948 who was trying to figure out how to shoot down a plane. The deeper insight was what he proved about living systems and machines. Before Wiener, biology and engineering were treated as completely separate domains. Organisms adapted. Machines calculated. The idea that you could describe both using the same mathematical framework was not just unusual. It was considered a category error. Wiener proved it anyway. He showed that a brain correcting a reaching movement and a missile correcting its trajectory were running mathematically identical control loops. The hardware was different. The math was the same. Living systems and engineered systems obeyed the same laws once you understood what those laws actually were. He named the field after the Greek word for steersman. Kubernetes. Cybernetics. The person who holds the rudder, reads the water, and adjusts constantly to hold a course through a current that is always pushing the ship somewhere else. That is the mental image he wanted. Not a machine that executes instructions. A system that responds to its own results. The third thing he did is the part almost nobody connects to modern AI. In 1948, Wiener spent an entire chapter of Cybernetics warning about what would happen when machines that learn from feedback were given control over consequential decisions. He described the displacement of workers not as a distant possibility but as a near-term certainty. He wrote about the ethical risks of building systems that optimize for measurable proxies of human values rather than actual human values. He described in plain language what alignment researchers today call Goodhart's Law without using that name, 25 years before Charles Goodhart published anything. He was a mathematician in 1948 writing about problems that AI safety researchers are still trying to solve in 2026. The book is dense in places. The equations are real and the sections on statistical mechanics require actual attention. But Wiener knew this, which is why in 1950 he published The Human Use of Human Beings, which is the same book with all the math removed. Same ideas. Same warnings. Written for anyone who reads English. That second book has been in print for 75 years and almost nobody in tech has read it. Wiener died in 1964 at a conference in Stockholm. He collapsed mid-conversation between sessions. He was 69. He did not live to see a personal computer. He did not live to see the internet. He never saw reinforcement learning, neural networks, or the AI systems that run almost entirely on the mathematical architecture he designed while trying to solve a World War II gunnery problem. Every AI lab in the world today is building systems that run on his framework. Almost none of the people building those systems know his name. The field he founded, cybernetics, mostly disappeared as a word. The ideas did not disappear. They dissolved into every other field. Control theory. Cognitive science. Computer science. Neuroscience. AI. They each took a piece of what he built and called it their own terminology. The word that survived is the one that proves he invented it. Feedback. You use it every day. You use it in code reviews, in meetings, in conversations about AI performance. Every time you use it in the technical sense, meaning a signal that closes a loop between output and goal, you are using the exact definition Wiener wrote down in 1948. He gave the word its meaning. Most people using it have never heard of him. The Human Use of Human Beings is free on archive. Cybernetics is in print and available anywhere books are sold. His major essays are in academic archives at no cost. The man who built the foundation of modern AI was writing about its dangers before the first commercial computer existed. Most people building AI today have never read a word he wrote.

‼️How Elite U.S. Journalists Took All-Expenses-Paid Trips to China — and Came Back Changed On July 28, 2026, White House correspondent Natalie Winters dropped a bombshell investigation that ripped the polite mask off a carefully engineered influence operation. A leaked internal document from the **Committee of 100 (C100)** — an organization Xi Jinping himself once praised as a “friendly organization” making “untiring efforts” to advance Chinese influence in the United States — revealed that at least 26 top American journalists, editors, and broadcasters had been flown to China on curated, high-end trips. The goal was not cultural exchange. It was to reshape how they saw Beijing… and how they covered it. ### The Operation C100’s “Leadership Delegation Program” targeted the people who decide what America reads and hears about China. Delegates came from *The New York Times*, *The Washington Post*, *Politico*, *NPR*, *PBS*, *The Atlantic*, *The New Yorker*, *Financial Times*, *TIME*, *USA Today*, and more. These were not backpacker trips. One 2012 delegation alone — Beijing, Shanghai, Hangzhou — cost roughly $60,000. Participants stayed in classic hotels, sampled regional Chinese cuisine, toured the Great Wall and Forbidden City, attended dinners in the Great Hall of the People, and sat down with carefully selected officials, state media executives, business leaders, and academics. C100 tracked its success with clinical precision. It publicly claimed that 70% of American “opinion leaders” who visited China returned with improved views of the country. Internally, the real metric was simpler: Did we change how they think? ### The Smoking-Gun Report The most damning piece is a 2012 after-action report marked **“Internal Use.”** It records what the journalists said after the trip: - Financial Times editor Gary Silverman: “I don’t think that I will ever think or write about China in the future without reflecting on what I learned this week.” - WNYC host Brian Lehrer: His perception of China changed “in 100 ways.” - Foreign Affairs managing editor Jonathan Tepperman returned with a “much deeper, more subtle and more nuanced sense” of China and called it the best press trip he had ever taken. - New York Times deputy business editor Winnie O’Kelley came back with “several story ideas” and plans to “shape my staff across Asia in some different ways.” She even hoped to add at least one business reporter in China. A Chinese academic congratulated the organizers: “It is hard to speak on China without being to the country. You made the change!” C100’s own verdict was blunt: “The C-100 Leadership Delegation Program has had a visible impact on their understanding and perceptions of China.” They hoped the delegates would share those new perceptions with colleagues and reshape coverage back home. ### The Names Here are some of the participants (titles at the time of their trips): - Jill Abramson — Managing Editor, *The New York Times* - David Brooks — Columnist, *The New York Times* - David Ignatius — Associate Editor & columnist, *Washington Post* - Eugene Robinson — Columnist & Associate Editor, *Washington Post* - Ruth Marcus — Columnist, *Washington Post* - Fred Hiatt — Editorial Page Editor, *Washington Post* - John Harris — Editor-in-Chief, *Politico* - Juan Williams — Senior Political Analyst, *NPR* - Brian Lehrer — Host, WNYC - Clive Crook — Senior Editor, *The Atlantic* - Jonathan Tepperman — Managing Editor, *Foreign Affairs* - Gary Silverman — U.S. News Editor, *Financial Times* - And many more from *Newsweek*, *TIME*, *Los Angeles Times*, *USA Today*, *HuffPost*, and *The New Yorker*. ### What Happened After They Came Home Winters tracked the subsequent work of several delegates. Some of the same voices later became prominent critics of tougher policies toward Beijing: - David Brooks called Trump’s proposed tariffs “the single worst policy idea on the table.” - Eugene Robinson described the trade war as an “ill-advised gambit” and suggested Xi Jinping could emerge as the “reasonable adult.” - David Ignatius warned America was “dramatically overestimating China’s capabilities” and called growing alarm “scare talk.” One Financial Times columnist later defended allowing the Chinese-founded fast-fashion giant Shein to list in London despite serious supply-chain and labor concerns. ### The Bigger Picture This was not random tourism. C100 has repeatedly been linked to figures and institutions inside the Chinese Communist Party’s United Front system — the same influence network U.S. officials describe as designed to “co-opt and neutralize sources of opposition.” The organization selected journalists based on their ability to deliver favorable coverage and then measured success by whether those journalists’ thinking actually shifted. The documents, itineraries, and internal assessments were scattered across archives for years. Natalie Winters pulled them together, named the names, and published the evidence. Full investigation and original documents: