Navigating the dense urban canyons of cities like San Francisco or New York can be a nightmare for GPS systems. The towering skyscrapers block and reflect satellite signals, leading to location errors of tens of meters. For you and me, that might mean a missed turn. But for an autonomous vehicle or a delivery robot, that level of imprecision is the difference between a successful mission and a costly failure. These machines require pinpoint accuracy to operate safely and efficiently. Addressing this critical challenge, researchers from the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland have introduced a groundbreaking new method for visual localization during CVPR 2025 Their new paper, “FG2: Fine-Grained Cross-View Localization by Fine-Grained Feature Matching,” presents a novel AI model that significantly enhances the ability of a ground-level system, like an autonomous car, to determine its exact position and orientation using only a camera and a corresponding aerial (or satellite) image. The new approach has demonstrated a remarkable 28% reduction in mean localization error compared to the previous state-of-the-art on a challenging public dataset. Key Takeaways: Superior Accuracy: The FG2 model reduces the average localization error by a significant 28% on the VIGOR cross-area test set, a challenging benchmark for this task. Human-like Intuition: Instead of relying on abstract descriptors, the model mimics human reasoning by matching fine-grained, semantically consistent features—like curbs, crosswalks, and buildings—between a ground-level photo and an aerial map. Enhanced Interpretability: The method allows researchers to “see” what the AI is “thinking” by visualizing exactly which features in the ground and aerial images are being matched, a major step forward from previous “black box” models. Weakly Supervised Learning: Remarkably, the model learns these complex and consistent feature matches without any direct labels for correspondences. It achieves this using only the final camera pose as a supervisory signal. Challenge: Seeing the World from Two Different Angles The core problem of cross-view localization is the dramatic difference in perspective between a street-level camera and an overhead satellite view. A building facade seen from the ground looks completely different from its rooftop signature in an aerial image. Existing methods have struggled with this. Some create a general “descriptor” for the entire scene, but this is an abstract approach that doesn’t mirror how humans naturally localize themselves by spotting specific landmarks. Other methods transform the ground image into a Bird’s-Eye-View (BEV) but are often limited to the ground plane, ignoring crucial vertical structures like buildings. FG2: Matching Fine-Grained Features The EPFL team’s FG2 method introduces a more intuitive and effective process. It aligns two sets of points: one generated from the ground-level image and another sampled from the aerial map. Here’s a breakdown of their innovative pipeline: Mapping to 3D: The process begins by taking the features from the ground-level image and lifting them into a 3D point cloud centered around the camera. This creates a 3D representation of the immediate environment. Smart Pooling to BEV: This is where the magic happens. Instead of simply flattening the 3D data, the model learns to intelligently select the most important features along the vertical (height) dimension for each point. It essentially asks, “For this spot on the map, is the ground-level road marking more important, or is the edge of that building’s roof the better landmark?” This selection process is crucial, as it allows the model to correctly associate features like building facades with their corresponding rooftops in the aerial view. Feature Matching and Pose Estimation: Once both the ground and aerial views are represented as 2D point planes with rich feature descriptors, the model computes the similarity between them. It then samples a sparse set of the most confident matches and uses a classic geometric algorithm called Procrustes alignment to calculate the precise 3-DoF (x, y, and yaw) pose. Unprecedented Performance and Interpretability The results speak for themselves. On the challenging VIGOR dataset, which includes images from different cities in its cross-area test, FG2 reduced the mean localization error by 28% compared to the previous best method. It also demonstrated superior generalization capabilities on the KITTI dataset, a staple in autonomous driving research. Perhaps more importantly, the FG2 model offers a new level of transparency. By visualizing the matched points, the researchers showed that the model learns semantically consistent correspondences without being explicitly told to. For example, the system correctly matches zebra crossings, road markings, and even building facades in the ground view to their corresponding locations on the aerial map. This interpretability is extremenly valuable for building trust in safety-critical autonomous systems. “A Clearer Path” for Autonomous Navigation The FG2 method represents a significant leap forward in fine-grained visual localization. By developing a model that intelligently selects and matches features in a way that mirrors human intuition, the EPFL researchers have not only shattered previous accuracy records but also made the decision-making process of the AI more interpretable. This work paves the way for more robust and reliable navigation systems for autonomous vehicles, drones, and robots, bringing us one step closer to a future where machines can confidently navigate our world, even when GPS fails them. Check out the Paper. All credit for this research goes to the researchers of this project. Also, feel free to follow us on Twitter and don’t forget to join our 100k+ ML SubReddit and Subscribe to our Newsletter. Jean-marc MommessinJean-marc is a successful AI business executive .He leads and accelerates growth for AI powered solutions and started a computer vision company in 2006. He is a recognized speaker at AI conferences and has an MBA from Stanford.Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/AI-Generated Ad Created with Google’s Veo3 Airs During NBA Finals, Slashing Production Costs by 95%Jean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Highlighted at CVPR 2025: Google DeepMind’s ‘Motion Prompting’ Paper Unlocks Granular Video ControlJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Snowflake Charts New AI Territory: Cortex AISQL & Snowflake Intelligence Poised to Reshape Data AnalyticsJean-marc Mommessinhttps://www.marktechpost.com/author/jean-marc0000677/Exclusive Talk: Joey Conway of NVIDIA on Llama Nemotron Ultra and Open Source Models

Generative AI has reshaped how people create, imagine and interact with digital content. As AI models continue to grow in capability and complexity, they require more VRAM, or video random access memory. The base Stable Diffusion 3.5 Large model, for example, uses over 18GB of VRAM — limiting the number of systems that can run it well. By applying quantization to the model, noncritical layers can be removed or run with lower precision. NVIDIA GeForce RTX 40 Series and the Ada Lovelace generation of NVIDIA RTX PRO GPUs support FP8 quantization to help run these quantized models, and the latest-generation NVIDIA Blackwell GPUs also add support for FP4. NVIDIA collaborated with Stability AI to quantize its latest model, Stable Diffusion (SD) 3.5 Large, to FP8 — reducing VRAM consumption by 40%. Further optimizations to SD3.5 Large and Medium with the NVIDIA TensorRT software development kit (SDK) double performance. In addition, TensorRT has been reimagined for RTX AI PCs, combining its industry-leading performance with just-in-time (JIT), on-device engine building and an 8x smaller package size for seamless AI deployment to more than 100 million RTX AI PCs. TensorRT for RTX is now available as a standalone SDK for developers. RTX-Accelerated AI NVIDIA and Stability AI are boosting the performance and reducing the VRAM requirements of Stable Diffusion 3.5, one of the world’s most popular AI image models. With NVIDIA TensorRT acceleration and quantization, users can now generate and edit images faster and more efficiently on NVIDIA RTX GPUs. Stable Diffusion 3.5 quantized FP8 (right) generates images in half the time with similar quality as FP16 (left). Prompt: A serene mountain lake at sunrise, crystal clear water reflecting snow-capped peaks, lush pine trees along the shore, soft morning mist, photorealistic, vibrant colors, high resolution. To address the VRAM limitations of SD3.5 Large, the model was quantized with TensorRT to FP8, reducing the VRAM requirement by 40% to 11GB. This means five GeForce RTX 50 Series GPUs can run the model from memory instead of just one. SD3.5 Large and Medium models were also optimized with TensorRT, an AI backend for taking full advantage of Tensor Cores. TensorRT optimizes a model’s weights and graph — the instructions on how to run a model — specifically for RTX GPUs. FP8 TensorRT boosts SD3.5 Large performance by 2.3x vs. BF16 PyTorch, with 40% less memory use. For SD3.5 Medium, BF16 TensorRT delivers a 1.7x speedup. Combined, FP8 TensorRT delivers a 2.3x performance boost on SD3.5 Large compared with running the original models in BF16 PyTorch, while using 40% less memory. And in SD3.5 Medium, BF16 TensorRT provides a 1.7x performance increase compared with BF16 PyTorch. The optimized models are now available on Stability AI’s Hugging Face page. NVIDIA and Stability AI are also collaborating to release SD3.5 as an NVIDIA NIM microservice, making it easier for creators and developers to access and deploy the model for a wide range of applications. The NIM microservice is expected to be released in July. TensorRT for RTX SDK Released Announced at Microsoft Build — and already available as part of the new Windows ML framework in preview — TensorRT for RTX is now available as a standalone SDK for developers. Previously, developers needed to pre-generate and package TensorRT engines for each class of GPU — a process that would yield GPU-specific optimizations but required significant time. With the new version of TensorRT, developers can create a generic TensorRT engine that’s optimized on device in seconds. This JIT compilation approach can be done in the background during installation or when they first use the feature. The easy-to-integrate SDK is now 8x smaller and can be invoked through Windows ML — Microsoft’s new AI inference backend in Windows. Developers can download the new standalone SDK from the NVIDIA Developer page or test it in the Windows ML preview. For more details, read this NVIDIA technical blog and this Microsoft Build recap. Join NVIDIA at GTC Paris At NVIDIA GTC Paris at VivaTech — Europe’s biggest startup and tech event — NVIDIA founder and CEO Jensen Huang yesterday delivered a keynote address on the latest breakthroughs in cloud AI infrastructure, agentic AI and physical AI. Watch a replay. GTC Paris runs through Thursday, June 12, with hands-on demos and sessions led by industry leaders. Whether attending in person or joining online, there’s still plenty to explore at the event. Each week, the RTX AI Garage blog series features community-driven AI innovations and content for those looking to learn more about NVIDIA NIM microservices and AI Blueprints, as well as building AI agents, creative workflows, digital humans, productivity apps and more on AI PCs and workstations.  Plug in to NVIDIA AI PC on Facebook, Instagram, TikTok and X — and stay informed by subscribing to the RTX AI PC newsletter. Follow NVIDIA Workstation on LinkedIn and X.  See notice regarding software product information.

Artificial intelligence is already rewriting the rules of wireless and telecom — powering predictive maintenance, streamlining network operations, and enabling more innovative services. As AI scales, the disruption will be faster, deeper, and harder to reverse than any prior shift in the industry. Compared to the sweeping changes AI is set to unleash, past telecom innovations look incremental. AI is redefining how networks operate, services are delivered, and data is secured — across every device and digital touchpoint. AI Is Reshaping Wireless Networks Already Artificial intelligence is already transforming wireless through smarter private networks, fixed wireless access (FWA), and intelligent automation across the stack. AI detects and resolves network issues before they impact service, improving uptime and customer satisfaction. It’s also opening the door to entirely new revenue streams and business models. Each wireless generation brought new capabilities. AI, however, marks a more profound shift — networks that think, respond, and evolve in real time. AI Acceleration Will Outpace Past Tech Shifts Many may underestimate the speed and magnitude of AI-driven change. The shift from traditional voice and data systems to AI-driven network intelligence is already underway. Although predictions abound, the true scope remains unclear. It’s tempting to assume we understand AI’s trajectory, but history suggests otherwise. Today, AI is already automating maintenance and optimizing performance without user disruption. The technologies we’ll rely on in the near future may still be on the drawing board. Few predicted that smartphones would emerge from analog beginnings—a reminder of how quickly foundational technologies can be reimagined. History shows that disruptive technologies rarely follow predictable paths — and AI is no exception. It’s already upending business models across industries. Technological shifts bring both new opportunities and complex trade-offs. AI Disruption Will Move Faster Than Ever The same cycle of reinvention is happening now — but with AI, it’s moving at unprecedented speed. Despite all the discussion, many still treat AI as a future concern — yet the shift is already well underway. As with every major technological leap, there will be gains and losses. The AI transition brings clear trade-offs: efficiency and innovation on one side, job displacement, and privacy erosion on the other. Unlike past tech waves that unfolded over decades, the AI shift will reshape industries in just a few years — and that change wave will only continue to move forward. AI Will Reshape All Sectors and Companies This shift will unfold faster than most organizations or individuals are prepared to handle. Today’s industries will likely look very different tomorrow. Entirely new sectors will emerge as legacy models become obsolete — redefining market leadership across industries. Telecom’s past holds a clear warning: market dominance can vanish quickly when companies ignore disruption. Eventually, the Baby Bells moved into long-distance service, while AT&T remained barred from selling local access — undermining its advantage. As the market shifted and competitors gained ground, AT&T lost its dominance and became vulnerable enough that SBC, a former regional Bell, acquired it and took on its name. It’s a case study of how incumbents fall when they fail to adapt — precisely the kind of pressure AI is now exerting across industries. SBC’s acquisition of AT&T flipped the power dynamic — proof that size doesn’t protect against disruption. The once-crowded telecom field has consolidated into just a few dominant players — each facing new threats from AI-native challengers. Legacy telecom models are being steadily displaced by faster, more flexible wireless, broadband, and streaming alternatives. No Industry Is Immune From AI Disruption AI will accelerate the next wave of industrial evolution — bringing innovations and consequences we’re only beginning to grasp. New winners will emerge as past leaders struggle to hang on — a shift that will also reshape the investment landscape. Startups leveraging AI will likely redefine leadership in sectors where incumbents have grown complacent. Nvidia’s rise is part of a broader trend: the next market leaders will emerge wherever AI creates a clear competitive advantage — whether in chips, code, or entirely new markets. The AI-driven future is arriving faster than most organizations are ready for. Adapting to this accelerating wave of change is no longer optional — it’s essential. Companies that act decisively today will define the winners of tomorrow.

Elon Musk may be gone from the Trump administration — and his friendship status with President Donald Trump may be at best uncertain — but his whirlwind stint in government certainly left its imprint. The Department of Government Efficiency (DOGE), his pet government-slashing project, remains entrenched in Washington. During his 130-day tenure, Musk led DOGE in eliminating about 260,000 federal employee jobs and gutting agencies supporting scientific research and humanitarian aid. But to date, DOGE claims to have saved the government $180 billion — well short of its ambitious (and frankly never realistic) target of cutting at least $2 trillion from the federal budget. And with Musk’s departure still fresh, there are reports that the federal government is trying to rehire federal workers who quit or were let go. For Elaine Kamarck, senior fellow at the Brookings Institution, DOGE’s tactics will likely end up being disastrous in the long run. “DOGE came in with these huge cuts, which were not attached to a plan,” she told Today, Explained co-host Sean Rameswaram. Kamarck knows all about making government more efficient. In the 1990s, she ran the Clinton administration’s Reinventing Government program. “I was Elon Musk,” she told Today, Explained. With the benefit of that experience, she assesses Musk’s record at DOGE, and what, if anything, the billionaire’s loud efforts at cutting government spending added up to. Below is an excerpt of the conversation, edited for length and clarity. There’s much more in the full podcast, so listen to Today, Explained wherever you get podcasts, including Apple Podcasts, Pandora, and Spotify. What do you think Elon Musk’s legacy is? Well, he will not have totally, radically reshaped the federal government. Absolutely not. In fact, there’s a high probability that on January 20, 2029, when the next president takes over, the federal government is about the same size as it is now, and is probably doing the same stuff that it’s doing now. What he did manage to do was insert chaos, fear, and loathing into the federal workforce. There was reporting in the Washington Post late last week that these cuts were so ineffective that the White House is actually reaching out to various federal employees who were laid off and asking them to come back, from the FDA to the IRS to even USAID. Which cuts are sticking at this point and which ones aren’t?First of all, in a lot of cases, people went to court and the courts have reversed those earlier decisions. So the first thing that happened is, courts said, “No, no, no, you can’t do it this way. You have to bring them back.” The second thing that happened is that Cabinet officers started to get confirmed by the Senate. And remember that a lot of the most spectacular DOGE stuff was happening in February. In February, these Cabinet secretaries were preparing for their Senate hearings. They weren’t on the job. Now that their Cabinet secretary’s home, what’s happening is they’re looking at these cuts and they’re saying, “No, no, no! We can’t live with these cuts because we have a mission to do.”As the government tries to hire back the people they fired, they’re going to have a tough time, and they’re going to have a tough time for two reasons. First of all, they treated them like dirt, and they’ve said a lot of insulting things. Second, most of the people who work for the federal government are highly skilled. They’re not paper pushers. We have computers to push our paper, right? They’re scientists. They’re engineers. They’re people with high skills, and guess what? They can get jobs outside the government. So there’s going to be real lasting damage to the government from the way they did this. And it’s analogous to the lasting damage that they’re causing at universities, where we now have top scientists who used to invent great cures for cancer and things like that, deciding to go find jobs in Europe because this culture has gotten so bad.What happens to this agency now? Who’s in charge of it?Well, what they’ve done is DOGE employees have been embedded in each of the organizations in the government, okay? And they basically — and the president himself has said this — they basically report to the Cabinet secretaries. So if you are in the Transportation Department, you have to make sure that Sean Duffy, who’s the secretary of transportation, agrees with you on what you want to do. And Sean Duffy has already had a fight during a Cabinet meeting with Elon Musk. You know that he has not been thrilled with the advice he’s gotten from DOGE. So from now on, DOGE is going to have to work hand in hand with Donald Trump’s appointed leaders.And just to bring this around to what we’re here talking about now, they’re in this huge fight over wasteful spending with the so-called big, beautiful bill. Does this just look like the government as usual, ultimately?It’s actually worse than normal. Because the deficit impacts are bigger than normal. It’s adding more to the deficit than previous bills have done. And the second reason it’s worse than normal is that everybody is still living in a fantasy world. And the fantasy world says that somehow we can deal with our deficits by cutting waste, fraud, and abuse. That is pure nonsense. Let me say it: pure nonsense.Where does most of the government money go? Does it go to some bureaucrats sitting on Pennsylvania Avenue? It goes to us. It goes to your grandmother and her Social Security and her Medicare. It goes to veterans in veterans benefits. It goes to Americans. That’s why it’s so hard to cut it. It’s so hard to cut it because it’s us. And people are living on it. Now, there’s a whole other topic that nobody talks about, and it’s called entitlement reform, right? Could we reform Social Security? Could we make the retirement age go from 67 to 68? That would save a lot of money. Could we change the cost of living? Nobody, nobody, nobody is talking about that. And that’s because we are in this crazy, polarized environment where we can no longer have serious conversations about serious issues. See More:

Fewer food crops Biofuels policy has been a failure for the climate, new report claims Report: An expansion of biofuels policy under Trump would lead to more greenhouse gas emissions. Georgina Gustin, Inside Climate News – Jun 14, 2025 7:10 am | 24 An ethanol production plant on March 20, 2024 near Ravenna, Nebraska. Credit: David Madison/Getty Images An ethanol production plant on March 20, 2024 near Ravenna, Nebraska. Credit: David Madison/Getty Images Story text Size Small Standard Large Width * Standard Wide Links Standard Orange * Subscribers only   Learn more This article originally appeared on Inside Climate News, a nonprofit, non-partisan news organization that covers climate, energy, and the environment. Sign up for their newsletter here. The American Midwest is home to some of the richest, most productive farmland in the world, enabling its transformation into a vast corn- and soy-producing machine—a conversion spurred largely by decades-long policies that support the production of biofuels. But a new report takes a big swing at the ethanol orthodoxy of American agriculture, criticizing the industry for causing economic and social imbalances across rural communities and saying that the expansion of biofuels will increase greenhouse gas emissions, despite their purported climate benefits. The report, from the World Resources Institute, which has been critical of US biofuel policy in the past, draws from 100 academic studies on biofuel impacts. It concludes that ethanol policy has been largely a failure and ought to be reconsidered, especially as the world needs more land to produce food to meet growing demand. “Multiple studies show that US biofuel policies have reshaped crop production, displacing food crops and driving up emissions from land conversion, tillage, and fertilizer use,” said the report’s lead author, Haley Leslie-Bole. “Corn-based ethanol, in particular, has contributed to nutrient runoff, degraded water quality and harmed wildlife habitat. As climate pressures grow, increasing irrigation and refining for first-gen biofuels could deepen water scarcity in already drought-prone parts of the Midwest.” The conversion of Midwestern agricultural land has been sweeping. Between 2004 and 2024, ethanol production increased by nearly 500 percent. Corn and soybeans are now grown on 92 and 86 million acres of land respectively—and roughly a third of those crops go to produce ethanol. That means about 30 million acres of land that could be used to grow food crops are instead being used to produce ethanol, despite ethanol only accounting for 6 percent of the country’s transportation fuel. The biofuels industry—which includes refiners, corn and soy growers and the influential agriculture lobby writ large—has long insisted that corn- and soy-based biofuels provide an energy-efficient alternative to fossil-based fuels. Congress and the US Department of Agriculture have agreed. The country’s primary biofuels policy, the Renewable Fuel Standard, requires that biofuels provide a greenhouse gas reduction over fossil fuels: The law says that ethanol from new plants must deliver a 20 percent reduction in greenhouse gas emissions compared to gasoline. In addition to greenhouse gas reductions, the industry and its allies in Congress have also continued to say that ethanol is a primary mainstay of the rural economy, benefiting communities across the Midwest. But a growing body of research—much of which the industry has tried to debunk and deride—suggests that ethanol actually may not provide the benefits that policies require. It may, in fact, produce more greenhouse gases than the fossil fuels it was intended to replace. Recent research says that biofuel refiners also emit significant amounts of carcinogenic and dangerous substances, including hexane and formaldehyde, in greater amounts than petroleum refineries. The new report points to research saying that increased production of biofuels from corn and soy could actually raise greenhouse gas emissions, largely from carbon emissions linked to clearing land in other countries to compensate for the use of land in the Midwest. On top of that, corn is an especially fertilizer-hungry crop requiring large amounts of nitrogen-based fertilizer, which releases huge amounts of nitrous oxide when it interacts with the soil. American farming is, by far, the largest source of domestic nitrous oxide emissions already—about 50 percent. If biofuel policies lead to expanded production, emissions of this enormously powerful greenhouse gas will likely increase, too. The new report concludes that not only will the expansion of ethanol increase greenhouse gas emissions, but it has also failed to provide the social and financial benefits to Midwestern communities that lawmakers and the industry say it has. (The report defines the Midwest as Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin.) “The benefits from biofuels remain concentrated in the hands of a few,” Leslie-Bole said. “As subsidies flow, so may the trend of farmland consolidation, increasing inaccessibility of farmland in the Midwest, and locking out emerging or low-resource farmers. This means the benefits of biofuels production are flowing to fewer people, while more are left bearing the costs.” New policies being considered in state legislatures and Congress, including additional tax credits and support for biofuel-based aviation fuel, could expand production, potentially causing more land conversion and greenhouse gas emissions, widening the gap between the rural communities and rich agribusinesses at a time when food demand is climbing and, critics say, land should be used to grow food instead. President Donald Trump’s tax cut bill, passed by the House and currently being negotiated in the Senate, would not only extend tax credits for biofuels producers, it specifically excludes calculations of emissions from land conversion when determining what qualifies as a low-emission fuel. The primary biofuels industry trade groups, including Growth Energy and the Renewable Fuels Association, did not respond to Inside Climate News requests for comment or interviews. An employee with the Clean Fuels Alliance America, which represents biodiesel and sustainable aviation fuel producers, not ethanol, said the report vastly overstates the carbon emissions from crop-based fuels by comparing the farmed land to natural landscapes, which no longer exist. They also noted that the impact of soy-based fuels in 2024 was more than $42 billion, providing over 100,000 jobs. “Ten percent of the value of every bushel of soybeans is linked to biomass-based fuel,” they said. Georgina Gustin, Inside Climate News 24 Comments

Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Submit your work ahead of the Final Entry Deadline on July 11th! Behind the term “paper architecture” hides a strange paradox: the radical act of building without, well, building. Paper architecture is usually associated with speculative design projects, presented in the form of drawings, which can also be considered art pieces. However, even though it is often dismissed as a mere utopian or academic exercise, paper architecture has historically served as a powerful form of protest, advocating against political regimes, architectural orthodoxy or cultural stagnation. Unbound by real-world limitations such as materials, regulations and budgets, paper architects are free to focus on the messages behind their designs rather than constantly striving for their implementation. In parallel, due to its subtleness, paper architecture has become a platform that enables radical commentary via a rather “safe” medium. Instead of relying on more traditional forms of protest (such as strikes or marches) this powerful visual language, combined with scrupulous aesthetics and imagination can start a more formidable “behind-the-scenes rebellion”. Unearthing Nostalgia by Bruno Xavier & Michelle Ashley Ovanessians, A+ Vision Awards, 2023 Perhaps the most well-known paper architects, Archigram was a radical British collective that was formed in the 1960s in London. Their work Walking City or Plug-In City showcased visions of a playful, technologically driven architecture that deeply contrasted and, by extent, protested against the rigid regime of post-war modernism and its extensive bureaucracy. This pop-art-style architecture served as a powerful critique towards the saturated idea of functional monotony. Additionally, the Russian architect, artist, and curator, Yuri Avvakumuv introduced the term “paper architecture” within the restrictive cultural and political climate of late Soviet Russia (1984). Having to deal with heavy censorship, Avvakumuv turned to competitions and speculative drawings in an attempt resist that dominance of totalitarian architecture. Poetic, deeply allegorical and oftentimes ironic architectural renderings, critiqued the bureaucratic sterility of Soviet planning and the state-mandated architectural principles architects had to follow. Consequently, this profound demonstration of un-built architecture within the specific setting, turned into a collective cultural wave that advocated artistic autonomy and expression for the built environment. Klothos’ Loom of Memories by Ioana Alexandra Enache, A+ Vision Awards, 2023 The Amerian architect Lebbeus Woods was also one of the most intellectually intense practitioners of paper architecture, whose work touches upon global issues on war zones and urban trauma. His imaginative, post-apocalyptic cities opened up discussions for rebuilding after destruction. Works such as War and Architecture and Underground Berlin, albeit “dystopic”, acted as moral propositions, exploring potential reconstructions that would “heal” these cities. Through his drawings, he rigorously investigated and examined scenarios of ethical rebuilding, refusing to comply to the principles of popular commerce, and instead creating a new architectural practice of political resistance. Finally, operating within a very male-dominated world, Zaha Hadid’s earlier work — particularly on Malevich — served as a protesting tool on multiple levels. Influenced by Suprematist aesthetics, her bold, dynamic compositions stood against the formal conservatism of architectural ideas, where the design must always yield to gravity and function. In parallel, her considerable influence and dominance on the field challenged long-standing norms and served as a powerful counter-narrative against the gender biases that sidelined women in design. Ultimately, her images – part blueprints, part paintings – not only proved that architecture could be unapologetically visionary and abstract but also that materializing it is not as impossible as one would think. (Your) My Bedroom by Daniel Wing-Hou Ho, A+ Vision Awards, 2023 Even though paper architecture began as a medium of rebellion against architectural convention in the mid-20th century, it remains, until today, a vital tool for activism and social justice. Operating in the digital age, social media and digital platforms have amplified its reach, also having given it different visual forms such as digital collages, speculative renders, gifs, reels and interactive visual narratives. What was once a flyer, a journal or a newspaper extract, can now be found in open-source repositories, standing against authoritarianism, climate inaction, political violence and systemic inequality. Groups such as Forensic Architecture (Goldsmiths, University of London)  carry out multidisciplinary research, investigating cases of state violence and violations of human rights through rigorous mapping and speculative visualization. Additionally, competitions such as the eVolo Skyscraper or platforms like ArchOutLoud and Design Earth offer opportunities and space for architects to tackle environmental concerns and dramatize the urgency of inaction. Imaginative floating habitats, food cities, biodegradable megastructures etc. instigate debates and conversations through the form of environmental storytelling. The Stamper Battery by By William du Toit, A+ Vision Awards, 2023 Despite being often condemned as “unbuildable”, “impractical” or even “escapist,” paper architecture acts as a counterweight to the discipline’s increasing instrumentalization as merely a functional or commercial enterprise. In architecture schools it is used as a prompt for “thinking differently” and a tool for “critiquing without compromise”. Above all however, paper architecture matters because it keeps architecture ethically alive. It reminds architects to ask the uncomfortable questions: how should we design for environmental sustainability, migrancy or social equality, instead of focusing on profit, convenience and spectacle? Similar to a moral compass or speculative mirror, unbuilt visions can trigger political, social and environmental turns that reshape not just how we build, but why we build at all. Architizer’s Vision Awards are back! The global awards program honors the world’s best architectural concepts, ideas and imagery. Submit your work ahead of the Final Entry Deadline on July 11th! Featured Image: Into the Void: Fragmented Time, Space, Memory, and Decay in Hiroshima by Victoria Wong, A+ Vision Awards 2023 The post Paper Architecture: From Soviet Subversion to Zaha’s Suprematism appeared first on Journal.