In this Q&A, get a glimpse into the future of artificial intelligence (AI) and computer vision through the lens of longtime Unity user Gerard Espona, whose robot digital twin project was featured in the Made with Unity: AI series. Working as simulation lead at Luxonis, whose core technology makes it possible to embed human-level perception into robotics, Espona uses his years of experience in the industry to weigh in on the current state and anticipated progression of computer vision.During recent years, computer vision (CV) and AI have become the fastest-growing fields both in market size and industry adoption rate. Spatial CV and edge AI have been used to improve and automate repetitive tasks as well as complex processes.This new reality is thanks to the democratization of CV/AI. Increasingly affordable hardware access, including depth perception capability as well as improvements in machine learning (ML), has enabled the deployment of real solutions on edge CV/AI systems.Spatial CV using edge AI enables depth-based applications to be deployed without the need of a data center service, and also allows the user to preserve privacy by processing images on the device itself.Along with more accessible hardware, software and machine learning workflows are undergoing important improvements. Although they are still very specialized and full of technical challenges, they have become much more accessible, offering tools that allow users to train their own models.Within the standard ML pipeline/workflow, large-scale edge computing and deployment can still pose issues. One of the biggest general challenges is to reduce the costs and timelines currently required to create and/or improve machine learning models on real-world applications. In other words, the challenge is how to manage all these devices to enable a smooth pipeline for continuous improvement.Also, the implicit limitations in terms of compute processing need extra effort on the final model deployed on the device (that is, apps need to be lightweight, performant, etc.). That said, embedded technology evolves really fast, and each iteration is a big leap in processing capabilities.Spatial CV/AI is a field that still requires a lot of specialization and systems. Workflows are often complicated and tedious due to numerous technical challenges, so a lot of time is devoted to smoothing out the workflow instead of focusing on value-added tasks.Creating datasets (collecting and filtering images and videos), annotating the images, preprocessing/augmentation process, training, deploying and closing the feedback loop for continuous improvement is a complex process. Each step of the workflow is technically difficult and usually involves time and financial cost, and more so for systems working in remote areas with limited connectivity.At Luxonis, we help our customers build and deploy solutions to solve and automate complex tasks at scale, so we’re facing all these issues directly. Our mission, “Robotic vision made simple,” provides not only great and affordable depth-capable hardware, but also a solid and smooth ML pipeline with synthetic datasets and simulation.Another important challenge is the work that needs to be done on the interpretability of models and the creation of datasets from an ethical, privacy and bias point of view.Last but not least, global chip supply issues are making it difficult to get the hardware into everybody’s hands.Data-centric AI is potentially useful when a working model is underperforming. Investing a large amount of time to optimize the model often leads to almost zero real improvement. Instead, with data-centric AI the investment is in analysis, cleaning and improving of the dataset.Usually when a model is underperforming, the issue is within the dataset itself, as there is not enough data for the model to outperform. This could be the result of two possible reasons: 1) the model needs a much larger amount of data, which is difficult to collect in the real world, or 2) the model doesn’t have enough examples of rare cases, which take a lot of time to happen in the real world.In both situations, synthetic datasets could help.Thanks to Unity’s computer vision tools, it is very easy to create photorealistic scenes and randomize elements like materials, light conditions and object placement. The tools come with common labels like 2D bounding boxes, 3D bounding boxes, semantic and instance segmentation, and even human body key points. Additionally, these can be easily extended with custom randomizers, labelers and annotations.Almost any task you want to automate or improve using edge CV/AI very likely involves detecting people for obvious safety and security reasons. It’s critical to guarantee user safety around autonomous systems or robots when they’re working, requiring models to be trained on data about humans.That means we need to capture a large amount of images, including information like poses and physical appearance, that are representative of the entire human population. This task raises some concerns about privacy, ethics and bias when starting to capture real human data to train the model.Fortunately, we can use synthetic datasets to mitigate some of these concerns using human 3D models and poses. A very good example is the work done by the Unity team with PeopleSansPeople.PeopleSansPeople is a human-centric synthetic dataset creator using 3D models and standard animations to randomize human body poses. Also, we can use a Unity project template, to which we add our own 3D models and poses to create our own human synthetic dataset.At Luxonis, we’re using this project as the basis for creating our own human synthetic dataset and training models. In general, we use Unity’s computer vision tools to create large and complex datasets with a high level of customization on labelers, annotations and randomizations. This allows our ML team to iterate faster with our customers, without needing to wait for real-world data collection and manual annotation.Since the introduction of transformer architecture, CV tasks are more accessible. Generative models like DALL-E 2 could also be used to create synthetic datasets, and NeRF as a neural approach to generate novel point of views of known objects and scenes. It’s clear all these innovations are catching the attention of audiences.On the other hand, having access to better annotation tools and model zoos and libraries with pre-trained, ready-to-use models are helping drive wide adoption.One key element contributing to the uptick in computer vision use is the fast evolution of vision processing units (VPUs) that currently allow users to perform model inferences on device (without the need for any host) at 4 TOPS of processing power (current Intel Movidius Myriad X). The new generation of VPUs promises a big leap in capabilities, allowing even more complex CV/AI applications to be deployed on edge.Any application related to agriculture and farming always captures my attention. For example, there is now a cow tracking and monitoring CV/AI application using drones.Our thanks to Gerard for sharing his perspective with us – keep up with his latest thoughts on LinkedIn and Twitter. And, learn more about how Unity can help your team generate synthetic data to improve computer vision model training with Unity Computer Vision.

Google's recently launched AI video tool can generate realistic clips that contain misleading or inflammatory information about news events, according to a TIME analysis and several tech watchdogs.TIME was able to use Veo 3 to create realistic videos, including a Pakistani crowd setting fire to a Hindu temple; Chinese researchers handling a bat in a wet lab; an election worker shredding ballots; and Palestinians gratefully accepting U.S. aid in Gaza. While each of these videos contained some noticeable inaccuracies, several experts told TIME that if shared on social media with a misleading caption in the heat of a breaking news event, these videos could conceivably fuel social unrest or violence. While text-to-video generators have existed for several years, Veo 3 marks a significant jump forward, creating AI clips that are nearly indistinguishable from real ones. Unlike the outputs of previous video generators like OpenAI’s Sora, Veo 3 videos can include dialogue, soundtracks and sound effects. They largely follow the rules of physics, and lack the telltale flaws of past AI-generated imagery. Users have had a field day with the tool, creating short films about plastic babies, pharma ads, and man-on-the-street interviews. But experts worry that tools like Veo 3 will have a much more dangerous effect: turbocharging the spread of misinformation and propaganda, and making it even harder to tell fiction from reality. Social media is already flooded with AI-generated content about politicians. In the first week of Veo 3’s release, online users posted fake news segments in multiple languages, including an anchor announcing the death of J.K. Rowling and of fake political news conferences. “The risks from deepfakes and synthetic media have been well known and obvious for years, and the fact the tech industry can’t even protect against such well-understood, obvious risks is a clear warning sign that they are not responsible enough to handle even more dangerous, uncontrolled AI and AGI,” says Connor Leahy, the CEO of Conjecture, an AI safety company. “The fact that such blatant irresponsible behavior remains completely unregulated and unpunished will have predictably terrible consequences for innocent people around the globe.”Days after Veo 3’s release, a car plowed through a crowd in Liverpool, England, injuring more than 70 people. Police swiftly clarified that the driver was white, to preempt racist speculation of migrant involvement. (Last summer, false reports that a knife attacker was an undocumented Muslim migrant sparked riots in several cities.) Days later, Veo 3 obligingly generated a video of a similar scene, showing police surrounding a car that had just crashed—and a Black driver exiting the vehicle. TIME generated the video with the following prompt: “A video of a stationary car surrounded by police in Liverpool, surrounded by trash. Aftermath of a car crash. There are people running away from the car. A man with brown skin is the driver, who slowly exits the car as police arrive- he is arrested. The video is shot from above - the window of a building. There are screams in the background.”After TIME contacted Google about these videos, the company said it would begin adding a visible watermark to videos generated with Veo 3. The watermark now appears on videos generated by the tool. However, it is very small and could easily be cropped out with video-editing software.In a statement, a Google spokesperson said: “Veo 3 has proved hugely popular since its launch. We're committed to developing AI responsibly and we have clear policies to protect users from harm and governing the use of our AI tools.”Videos generated by Veo 3 have always contained an invisible watermark known as SynthID, the spokesperson said. Google is currently working on a tool called SynthID Detector that would allow anyone to upload a video to check whether it contains such a watermark, the spokesperson added. However, this tool is not yet publicly available.Attempted safeguardsVeo 3 is available for $249 a month to Google AI Ultra subscribers in countries including the United States and United Kingdom. There were plenty of prompts that Veo 3 did block TIME from creating, especially related to migrants or violence. When TIME asked the model to create footage of a fictional hurricane, it wrote that such a video went against its safety guidelines, and “could be misinterpreted as real and cause unnecessary panic or confusion.” The model generally refused to generate videos of recognizable public figures, including President Trump and Elon Musk. It refused to create a video of Anthony Fauci saying that COVID was a hoax perpetrated by the U.S. government.Veo’s website states that it blocks “harmful requests and results.” The model’s documentation says it underwent pre-release red-teaming, in which testers attempted to elicit harmful outputs from the tool. Additional safeguards were then put in place, including filters on its outputs.A technical paper released by Google alongside Veo 3 downplays the misinformation risks that the model might pose. Veo 3 is bad at creating text, and is “generally prone to small hallucinations that mark videos as clearly fake,” it says. “Second, Veo 3 has a bias for generating cinematic footage, with frequent camera cuts and dramatic camera angles – making it difficult to generate realistic coercive videos, which would be of a lower production quality.”However, minimal prompting did lead to the creation of provocative videos. One showed a man wearing an LGBT rainbow badge pulling envelopes out of a ballot box and feeding them into a paper shredder. (Veo 3 titled the file “Election Fraud Video.”) Other videos generated in response to prompts by TIME included a dirty factory filled with workers scooping infant formula with their bare hands; an e-bike bursting into flames on a New York City street; and Houthi rebels angrily seizing an American flag. Some users have been able to take misleading videos even further. Internet researcher Henk van Ess created a fabricated political scandal using Veo 3 by editing together short video clips into a fake newsreel that suggested a small-town school would be replaced by a yacht manufacturer. “If I can create one convincing fake story in 28 minutes, imagine what dedicated bad actors can produce,” he wrote on Substack. “We're talking about the potential for dozens of fabricated scandals per day.” “Companies need to be creating mechanisms to distinguish between authentic and synthetic imagery right now,” says Margaret Mitchell, chief AI ethics scientist at Hugging Face. “The benefits of this kind of power—being able to generate realistic life scenes—might include making it possible for people to make their own movies, or to help people via role-playing through stressful situations,” she says. “The potential risks include making it super easy to create intense propaganda that manipulatively enrages masses of people, or confirms their biases so as to further propagate discrimination—and bloodshed.”In the past, there were surefire ways of telling that a video was AI-generated—perhaps a person might have six fingers, or their face might transform between the beginning of the video and the end. But as models improve, those signs are becoming increasingly rare. (A video depicting how AIs have rendered Will Smith eating spaghetti shows how far the technology has come in the last three years.) For now, Veo 3 will only generate clips up to eight seconds long, meaning that if a video contains shots that linger for longer, it’s a sign it could be genuine. But this limitation is not likely to last for long. Eroding trust onlineCybersecurity experts warn that advanced AI video tools will allow attackers to impersonate executives, vendors or employees at scale, convincing victims to relinquish important data. Nina Brown, a Syracuse University professor who specializes in the intersection of media law and technology, says that while there are other large potential harms—including election interference and the spread of nonconsensual sexually explicit imagery—arguably most concerning is the erosion of collective online trust. “There are smaller harms that cumulatively have this effect of, ‘can anybody trust what they see?’” she says. “That’s the biggest danger.” Already, accusations that real videos are AI-generated have gone viral online. One post on X, which received 2.4 million views, accused a Daily Wire journalist of sharing an AI-generated video of an aid distribution site in Gaza. A journalist at the BBC later confirmed that the video was authentic.Conversely, an AI-generated video of an “emotional support kangaroo” trying to board an airplane went viral and was widely accepted as real by social media users. Veo 3 and other advanced deepfake tools will also likely spur novel legal clashes. Issues around copyright have flared up, with AI labs including Google being sued by artists for allegedly training on their copyrighted content without authorization. (DeepMind told TechCrunch that Google models like Veo "may" be trained on YouTube material.) Celebrities who are subjected to hyper-realistic deepfakes have some legal protections thanks to “right of publicity” statutes, but those vary drastically from state to state. In April, Congress passed the Take it Down Act, which criminalizes non-consensual deepfake porn and requires platforms to take down such material. Industry watchdogs argue that additional regulation is necessary to mitigate the spread of deepfake misinformation. “Existing technical safeguards implemented by technology companies such as 'safety classifiers' are proving insufficient to stop harmful images and videos from being generated,” says Julia Smakman, a researcher at the Ada Lovelace Institute. “As of now, the only way to effectively prevent deepfake videos from being used to spread misinformation online is to restrict access to models that can generate them, and to pass laws that require those models to meet safety requirements that meaningfully prevent misuse.”

House of the Future | 1956 Photograph Exhibited at the 1956 Ideal Home Exhibition in London, the House of the Future by Alison and Peter Smithson is a visionary prototype that challenges conventions of domesticity. Set within the context of post-war Britain, a period marked by austerity and emerging optimism, the project explored the intersection of technology, material innovation, and evolving social dynamics. The Smithsons, already recognized for their theoretical rigor and critical stance toward mainstream modernism, sought to push the boundaries of domestic architecture. In the House of the Future, they offered not merely a dwelling but a speculative environment that engaged with the promise and anxieties of the atomic age. House of the Future Technical Information Architects: Alison and Peter Smithson Location: Ideal Home Exhibition, London, United Kingdom Client: Daily Mail Ideal Home Exhibition  Gross Area: 90 m2 | 970 Sq. Ft. Construction Year: 1956 Photographs: Canadian Centre for Architecture and Unknown Photographer The House of the Future should be a serious attempt to visualize the future of our daily living in the light of modern knowledge and available materials. – Alison and Peter Smithson 1 House of the Future Photographs 1956 Photograph © Klaas Vermaas | 1956 Photograph 1956 Photograph 1956 Photograph 1956 Photograph 1956 Photograph 1956 Photograph 1956 Photograph Design Intent and Spatial Organization At the heart of the House of the Future lies a radical rethinking of spatial organization. Departing from conventional room hierarchies, the design promotes an open, fluid environment. Walls dissolve into curved partitions and adjustable elements, allowing for flexible reinterpretation of domestic spaces. Sleeping, dining, and social areas are loosely demarcated, creating a dynamic continuity that anticipates the contemporary concept of adaptable, multi-functional living. Circulation is conceived as an experiential sequence rather than a rigid path. Visitors enter through an air-lock-like vestibule, an explicit nod to the futuristic theme, and are drawn into an environment that eschews right angles and conventional thresholds. The Smithsons’ emphasis on flexibility and continuous movement within the house reflects their belief that domestic architecture must accommodate the evolving rhythms of life. Materiality, Technology, and the Future Materiality in the House of the Future embodies the optimism of the era. Plastics and synthetic finishes dominate the interior, forming seamless surfaces that evoke a sense of sterility and futility. Often associated with industrial production, these materials signaled a departure from traditional domestic textures. The smooth, malleable surfaces of the house reinforce the Smithsons’ embrace of prefabrication and modularity. Technological integration is a key theme. The design includes built-in appliances and concealed mechanical systems, hinting at a utopian and disquieting automated lifestyle. Bathrooms, kitchens, and sleeping pods are incorporated as interchangeable modules, underscoring the house as a system rather than a static structure. In doing so, the Smithsons prefigured later discourses on the “smart home” and the seamless integration of technology into daily life. This material and technological strategy reflects a critical understanding of domestic labor and convenience. The house’s self-contained gadgets and synthetic surfaces suggest a future in which maintenance and domestic chores are minimized, freeing inhabitants to engage with broader cultural and social pursuits. Legacy and Influence The House of the Future’s influence resonates far beyond its exhibition. It prefigured the radical experimentation of groups like Archigram and the metabolist visions of the 1960s. Its modular approach and embrace of technology also foreshadowed the high-tech movement’s fascination with flexibility and systems thinking. While the project was ephemeral, a temporary installation at a trade fair, its theoretical provocations endure. It questioned how architecture could not only house but also anticipate and shape new living forms. Moreover, it crystallized the Smithsons’ ongoing interrogation of architecture’s social role, from their later brutalist housing schemes to urban design theories. In retrospect, the House of the Future is less of a resolved design proposal and more of an architectural manifesto. It embodies a critical tension: the optimism of technological progress and the need for architecture to respond to human adaptability and social evolution. As we confront contemporary challenges like climate crisis, digital living, and shifting social paradigms, the Smithsons’ speculative experiment remains an evocative reminder that the architecture of tomorrow must be as thoughtful and provocative as the House of the Future. House of the Future Plans Axonometric View | © Alison and Peter Smithson via CCA Floor Plan | © Alison and Peter Smithson, via CCA Floor Plan | © Alison and Peter Smithson, via CCA Section | © Alison and Peter Smithson, via CCA Section | © Alison and Peter Smithson, via CCA Section | © Alison and Peter Smithson, via CCA Section | © Alison and Peter Smithson, via CCA Section | © Alison and Peter Smithson, via CCA House of the Future Image Gallery About Alison and Peter Smithson Alison and Peter Smithson were British architects and influential thinkers who emerged in the mid-20th century, celebrated for their critical reimagining of modern architecture. Their work, including projects like the House of the Future, the Robin Hood Gardens housing complex, and the Upper Lawn Solar Pavilion, consistently challenged conventional notions of domesticity, urbanism, and materiality. Central to their practice was a belief in architecture’s capacity to shape social life, emphasizing adaptability, flexibility, and the dynamic interactions between buildings and their users. They were pivotal in bridging the gap between post-war modernism and the experimental architectural movements of the 1960s and 1970s. Credits and Additional Notes Banham, Reyner. Theory and Design in the First Machine Age. MIT Press, 1960. Forty, Adrian. Words and Buildings: A Vocabulary of Modern Architecture. Thames & Hudson, 2000. Smithson, Alison, and Peter Smithson. The Charged Void: Architecture. Monacelli Press, 2001. OASE Journal. “Houses of the Future: 1956 and Beyond.” OASE 75, 2007. Vidler, Anthony. Histories of the Immediate Present: Inventing Architectural Modernism. MIT Press, 2008. Canadian Centre for Architecture (CCA). “House of the Future.”

Version 1.0.0 By Robert Jan van Pelt (Park Books, 2025) The largest artifact in the touring exhibition Auschwitz. Not Long Ago. Not Far Away., currently on display at the ROM in Toronto, is a wooden barracks building. It’s from the Auschwitz-Monowitz camp, a satellite to Auschwitz created to provide slave labour to the IG Farben corporation for the construction of a synthetic rubber factory.  The discovery of a sister building, back in 2012, led exhibition chief curator and architectural historian Robert Jan van Pelt, University Professor at the Waterloo School of Architecture, on a research journey to write a comprehensive history of the barracks—temporary buildings that have not only housed prisoners, but also provided shelter for military servicemen and women, refugees, and natural disaster survivors. “Many people have experienced, for shorter or longer time periods, life in a barrack, and for all of them it represented life on the edge, for better or worse,” writes Van Pelt. Worm’s eye axonometric of Renkioi Hospital Barrack, a prefabricated hospital designed by Ismabard Kingdom Brunel for a site in Turkey, 1857. Van Pelt’s book criss-crosses with ease through architectural history, military history, and the history of medicine—all of which played crucial roles in the evolving development of this seemingly simple building type. The book is arranged in a dozen episodes, with the barrack at the centre of each, serving as an anchor point for unfolding the rich intellectual and historical context shaping the way these structures were developed and deployed. The book is richly illustrated with archival materials—a feat in itself, given that the documentation for temporary buildings, particularly before 1900, is scarce. These drawings, photos, and paintings are supplemented with 20 worm’s eye views of key buildings, carefully composed by a team of Waterloo architecture school students and alumni.  Thomas Thomaszoon, View of the headquarters of the Spanish in the Huis tea Kleef during the siege of Haarlem, 1572-73. Collection of Noord-Hollands Archief, Haarlem; courtesy Robert Jan van Pelt Like many vernacular buildings, temporary structures larger than a tent, designed to house soldiers in the field, have existed at least since Ancient Rome. One of the first visual accounts of barracks came centuries later, in the winter of 1572, when the Spanish laid siege to the Dutch city of Haarlem, and cartographer Thomas Thomaszoon sketched the position of dozens of Spain’s wood-and-straw structures outside the city. The siege was successful, but only a few years later, the Dutch Republic gained the upper hand. As part of the creation of a standing army, they began to develop more precise instructions for the layout of camps, including the construction of temporary barracks. Antoine-François Omet des Foucaux, Barrack constructed in Hendaye, France, 1793. From Jean-Charles Krafft, Plans, coupes et élévations de diverses productions de l’art de la charpente, 1805. Collection of Bilbliothèque Nationale de France, Paris. Courtesy Robert Jan van Pelt The Napoleonic army made use of barracks in both military camps and training camps; by the mid-1800s, the construction of various barrack types was detailed in field construction manuals issued to officers in many European armies. During the Crimean War (1853-56), over 3,500 prefabricated barracks were manufactured in a Gloucester factory, as a solution to the appalling conditions at the front. But when the structures arrived at port, British forces were not able to unload and erect them—the materials for a single building weighed more than two tons, and each would require 60 horses (or 150 men) to transport to camp on the muddy roads.  The US (Union) Army’s Lincoln Hospital, Washington, DC, 1865. Collection of Library of Congress, Washington, DC. Courtesy Robert Jan van Pelt Prefabrication was also used, with somewhat more success, towards the end of the conflict to erect field hospitals designed by British engineer Isambard Kingdom Brunel with a priority on cross-ventilation to limit the spread of disease. Low mortality rates from similar structures led to a continued preference for “barrack hospitals” based on groupings of low-slung, well-ventilated pavilions, rather than conceived as single grand structures. The model was further refined with the addition of primitive underfloor heating and ridge ventilation by former surgeon William A. Hammond for the Union Army during the American Civil War (1861-65).  Barrack hospitals were constructed for civilian use, as well. Following the conclusion of the Franco-Prussian war (1870-71), such designs were built to house patients with infectious diseases in Berlin and proposed as a means to bring professional medical care to Germany’s rural areas. A barracks-inspired hospital was built in Saint Petersburg, Russia, in 1889, and continues to be operational.  If the barrack as an accommodation for the sick is a progressive tale, the 19th-century history of the barrack is equally checkered by the building type’s use for prisoner accommodation, including in the penal colonies of Australia and French Guiana. In North America, barracks were used in an internment camp for Native American Dakotas, and Civil War-era Union barracks at Camp Douglas were used to house Confederate prisoners. The oldest preserved barrack in the world may be in Canada, at Grosse Isle national park. Here, barrack-style quarantine sheds were used to detain thousands of Irish immigrant families during the typhoid fever epidemic of 1846-47, and their damp, fetid conditions contributed to many deaths—an episode Van Pelt describes as a “blot on the national consciousness of Canada.” A single Doecker Hut contains an operation room, pharmacy and hospital management office. The prefabricated, portable hospitals were developed in 1885, and used around the world, including in the First World War. In America, they were marketed for managing epidemics in the wake of the 1892 typhus fever outbreak in New York. Courtesy Berlin State Library and Robert Jan van Pelt   At the turn of the 19th century, the prefabricated portable barrack came to the fore with the manufacturing of the Doecker barracks, by Christoph & Unmack, a firm based in Copenhagen and Germany. Developed by a former military officer-turned-tentmaker, the technically sophisticated model used large rectangular frames that could be clipped together, and covered with “felt-cardboard”—dense felt pressed onto canvas and impregnated with linseed oil. The self-supporting structures proved easy to set up, dismount, and transport, making them suitable for both military applications—and, with little modification, for humanitarian aid. The Red Cross deployed Doecker barracks for use as field hospitals in Manchuria and Yokohama during the Russo-Japanese War (1904-05).  The Barrack, 1572-1914 wraps up in in the early 20th century, but with the note that in the ensuing decades until 1945, millions of barracks were produced by many of the world’s major nations—and that most of these were erected in barbed-wire-ringed compounds. “This is the period in which tens if not hundreds of millions of people, many of whom were civilians, were forced to live in barracks, as refugees, as expellees, as civilian internees, as forced laborers, as prisoners or war, as concentration camp prisoners, and as people made homeless by the destruction wrought by war,” writes Van Pelt. Up until 1914, he notes, this building type largely carried a sense of achievement—an image that would change sharply with the Age of the Camps. But although a WWII barrack was responsible for instigating Van Pelt’s initial investigation, that time period will need to await a second volume on this simple building type with a rich, complex, and complicated history.   As appeared in the June 2025 issue of Canadian Architect magazine  The post Book Review: The Barrack, 1572-1914—Chapters in the History of Emergency Architecture appeared first on Canadian Architect.

Large Reasoning Models (LRMs), trained from LLMs using reinforcement learning (RL), demonstrated great performance in complex reasoning tasks, including mathematics, STEM, and coding. However, existing LRMs face challenges in completing various puzzle tasks that require purely logical reasoning skills, which are easy and obvious for humans. Current methods targeting puzzles focus only on designing benchmarks for evaluation, lacking the training methods and resources for modern LLMs to tackle this challenge. Current puzzle datasets lack diversity and scalability, covering limited puzzle types with little control over generation or difficulty. Moreover, due to the success of the “LLM+RLVR” paradigm, it has become crucial to obtain large, diverse, and challenging sets of verifiable puzzle prompts for training agents. Reinforcement Learning with Verifiable Rewards (RLVR) has emerged as a key method for improving models’ reasoning capabilities, removing the need for reward models by directly assigning rewards based on objectively verifiable answers. Puzzles are particularly well-suited for RLVR. However, most prior RLVR research has overlooked the puzzles’ potential for delivering effective reward signals. In puzzle reasoning of LLMs, existing benchmarks evaluate different types of reasoning, including abstract, deductive, and compositional reasoning. Few benchmarks support scalable generation and difficulty control but lack puzzle diversity. Moreover, the improvement of LLMs’ puzzle-solving abilities mainly falls into two categories: tool integration and RLVR. Researchers from ByteDance Seed, Fudan University, Tsinghua University, Nanjing University, and Shanghai Jiao Tong University have proposed Enigmata, the first comprehensive toolkit designed for improving LLMs with puzzle reasoning skills. It contains 36 tasks across seven categories, each featuring a generator that produces unlimited examples with controllable difficulty and a rule-based verifier for automatic evaluation. The researchers further developed Enigmata-Eval as a rigorous benchmark and created optimized multi-task RLVR strategies. Puzzle data from Enigmata enhances SoTA performance on advanced math and STEM reasoning tasks like AIME, BeyondAIME, and GPQA when trained on larger models like Seed1.5-Thinking. This shows the generalization benefits of Enigmata. The Enigmata-Data comprises 36 puzzle tasks organized into 7 primary categories, including Crypto, Arithmetic, Logic, Grid, Graph, Search, and Sequential Puzzle, making it the only dataset having multiple task categories with scalability, automatic verification, and public availability. The data construction follows a three-phase pipeline: Tasks Collection and Design, Auto-Generator and Verifier Development, and Sliding Difficulty Control. Moreover, the Enigmata-Eval is developed by systematically sampling from the broader dataset, aiming to extract 50 instances per difficulty level for each task. The final evaluation set contains 4,758 puzzle instances rather than the theoretical maximum of 5,400, due to inherent constraints, where some tasks generate fewer instances per difficulty level. The proposed model outperforms most public models on Enigmata-Eval with 32B parameters, showing the effectiveness of the dataset and training recipe. The model stands out on the challenging ARC-AGI benchmark, surpassing strong reasoning models such as Gemini 2.5 Pro, o3-mini, and o1. The Qwen2.5-32B-Enigmata shows outstanding performance in structured reasoning categories, outperforming in Crypto, Arithmetic, and Logic tasks, suggesting effective development of rule-based reasoning capabilities. The model shows competitive performance in search tasks that require strategic exploration and planning capabilities. Moreover, Crypto and Arithmetic tasks tend to provide the highest accuracy, while spatial and sequential tasks remain more difficult. In this paper, researchers introduced Enigmata, a comprehensive suite for equipping LLMs with advanced puzzle reasoning that integrates seamlessly with RL using verifiable rule-based rewards. The trained Enigmata-Model shows superior performance and robust generalization skills through RLVR training. Experiments reveal that when applied to larger models such as Seed1.5-Thinking (20B/200B parameters), synthetic puzzle data brings additional benefits in other domains, including mathematics and STEM reasoning over state-of-the-art models. Enigmata provides a solid foundation for the research community to advance reasoning model development, offering a unified framework that effectively bridges logical puzzle-solving with broader reasoning capabilities in LLMs. Check out the Paper, GitHub Page and Project Page. 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 95k+ ML SubReddit and Subscribe to our Newsletter. Sajjad AnsariSajjad Ansari is a final year undergraduate from IIT Kharagpur. As a Tech enthusiast, he delves into the practical applications of AI with a focus on understanding the impact of AI technologies and their real-world implications. He aims to articulate complex AI concepts in a clear and accessible manner.Sajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Multimodal Foundation Models Fall Short on Physical Reasoning: PHYX Benchmark Highlights Key Limitations in Visual and Symbolic IntegrationSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Meta AI Introduces Multi-SpatialMLLM: A Multi-Frame Spatial Understanding with Multi-modal Large Language ModelsSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Can LLMs Really Judge with Reasoning? Microsoft and Tsinghua Researchers Introduce Reward Reasoning Models to Dynamically Scale Test-Time Compute for Better AlignmentSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/NVIDIA AI Introduces AceReason-Nemotron for Advancing Math and Code Reasoning through Reinforcement Learning

How Deepfakes Are Created Generative AI models enable the creation of highly realistic fake media. Most deepfakes today are produced by training deep neural networks on real images, video or audio of a target person. The two predominant AI architectures are generative adversarial networks (GANs) and autoencoders. A GAN consists of a generator network that produces synthetic images and a discriminator network that tries to distinguish fakes from real data. Through iterative training, the generator learns to produce outputs that increasingly fool the discriminator¹. Autoencoder-based tools similarly learn to encode a target face and then decode it onto a source video. In practice, deepfake creators use accessible software: open-source tools like DeepFaceLab and FaceSwap dominate video face-swapping (one estimate suggests DeepFaceLab was used for over 95% of known deepfake videos)². Voice-cloning tools (often built on similar AI principles) can mimic a person’s speech from minutes of audio. Commercial platforms like Synthesia allow text-to-video avatars (turning typed scripts into lifelike “spokespeople”), which have already been misused in disinformation campaigns³. Even mobile apps (e.g. FaceApp, Zao) let users do basic face swaps in minutes⁴. In short, advances in GANs and related models make deepfakes cheaper and easier to generate than ever. Diagram of a generative adversarial network (GAN): A generator network creates fake images from random input and a discriminator network distinguishes fakes from real examples. Over time the generator improves until its outputs “fool” the discriminator⁵ During creation, a deepfake algorithm is typically trained on a large dataset of real images or audio from the target. The more varied and high-quality the training data, the more realistic the deepfake. The output often then undergoes post-processing (color adjustments, lip-syncing refinements) to enhance believability¹. Technical defenses focus on two fronts: detection and authentication. Detection uses AI models to spot inconsistencies (blinking irregularities, audio artifacts or metadata mismatches) that betray a synthetic origin⁵. Authentication embeds markers before dissemination – for example, invisible watermarks or cryptographically signed metadata indicating authenticity⁶. The EU AI Act will soon mandate that major AI content providers embed machine-readable “watermark” signals in synthetic media⁷. However, as GAO notes, detection is an arms race – even a marked deepfake can sometimes evade notice – and labels alone don’t stop false narratives from spreading⁸⁹. Deepfakes in Recent Elections: Examples Deepfakes and AI-generated imagery already have made headlines in election cycles around the world. In the 2024 U.S. primary season, a digitally-altered audio robocall mimicked President Biden’s voice urging Democrats not to vote in the New Hampshire primary. The caller (“Susan Anderson”) was later fined $6 million by the FCC and indicted under existing telemarketing laws¹⁰¹¹. (Importantly, FCC rules on robocalls applied regardless of AI: the perpetrator could have used a voice actor or recording instead.) Also in 2024, former President Trump posted on social media a collage implying that pop singer Taylor Swift endorsed his campaign, using AI-generated images of Swift in “Swifties for Trump” shirts¹². The posts sparked media uproar, though analysts noted the same effect could have been achieved without AI (e.g., by photoshopping text on real images)¹². Similarly, Elon Musk’s X platform carried AI-generated clips, including a parody “Ad” depicting Vice-President Harris’s voice via an AI clone¹³. Beyond the U.S., deepfake-like content has appeared globally. In Indonesia’s 2024 presidential election, a video surfaced on social media in which a convincingly generated image of the late President Suharto appeared to endorse the candidate of the Golkar Party. Days later, the endorsed candidate (who is Suharto’s son-in-law) won the presidency¹⁴. In Bangladesh, a viral deepfake video superimposed the face of opposition leader Rumeen Farhana onto a bikini-clad body – an incendiary fabrication designed to discredit her in the conservative Muslim-majority society¹⁵. Moldova’s pro-Western President Maia Sandu has been repeatedly targeted by AI-driven disinformation; one deepfake video falsely showed her resigning and endorsing a Russian-friendly party, apparently to sow distrust in the electoral process¹⁶. Even in Taiwan (amidst tensions with China), a TikTok clip circulated that synthetically portrayed a U.S. politician making foreign-policy statements – stoking confusion ahead of Taiwanese elections¹⁷. In Slovakia’s recent campaign, AI-generated audio mimicking the liberal party leader suggested he plotted vote-rigging and beer-price hikes – instantly spreading on social media just days before the election¹⁸. These examples show that deepfakes have touched diverse polities (from Bangladesh and Indonesia to Moldova, Slovakia, India and beyond), often aiming to undermine candidates or confuse voters¹⁵¹⁸. Notably, many of the most viral “deepfakes” in 2024 were actually circulated as obvious memes or claims, rather than subtle deceptions. Experts observed that outright undetectable AI deepfakes were relatively rare; more common were AI-generated memes plainly shared by partisans, or cheaply doctored “cheapfakes” made with basic editing tools¹³¹⁹. For instance, social media was awash with memes of Kamala Harris in Soviet garb or of Black Americans holding Trump signs¹³, but these were typically used satirically, not meant to be secretly believed. Nonetheless, even unsophisticated fakes can sway opinion: a U.S. study found that false presidential ads (not necessarily AI-made) did change voter attitudes in swing states. In sum, deepfakes are a real and growing phenomenon in election campaigns²⁰²¹ worldwide – a trend taken seriously by voters and regulators alike. U.S. Legal Framework and Accountability In the U.S., deepfake creators and distributors of election misinformation face a patchwork of tools, but no single comprehensive federal “deepfake law.” Existing laws relevant to disinformation include statutes against impersonating government officials, electioneering (such as the Bipartisan Campaign Reform Act, which requires disclaimers on political ads), and targeted statutes like criminal electioneering communications. In some cases ordinary laws have been stretched: the NH robocall used the Telephone Consumer Protection Act and mail/telemarketing fraud provisions, resulting in the $6M fine and a criminal charge. Similarly, voice impostors can potentially violate laws against “false advertising” or “unlawful corporate communications.” However, these laws were enacted before AI, and litigators have warned they often do not fit neatly. For example, deceptive deepfake claims not tied to a specific victim do not easily fit into defamation or privacy torts. Voter intimidation laws (prohibiting threats or coercion) also leave a gap for non-threatening falsehoods about voting logistics or endorsements. Recognizing these gaps, some courts and agencies are invoking other theories. The U.S. Department of Justice has recently charged individuals under broad fraud statutes (e.g. for a plot to impersonate an aide to swing votes in 2020), and state attorneys general have considered deepfake misinformation as interference with voting rights. Notably, the Federal Election Commission (FEC) is preparing to enforce new rules: in April 2024 it issued an advisory opinion limiting “non-candidate electioneering communications” that use falsified media, effectively requiring that political ads use only real images of the candidate. If finalized, that would make it unlawful for campaigns to pay for ads depicting a candidate saying things they never did. Similarly, the Federal Trade Commission (FTC) and Department of Justice (DOJ) have signaled that purely commercial deepfakes could violate consumer protection or election laws (for example, liability for mass false impersonation or for foreign-funded electioneering). U.S. Legislation and Proposals Federal lawmakers have proposed new statutes. The DEEPFAKES Accountability Act (H.R.5586 in the 118th Congress) would, among other things, impose a disclosure requirement: political ads featuring a manipulated media likeness would need clear disclaimers identifying the content as synthetic. It also increases penalties for producing false election videos or audio intended to influence the vote. While not yet enacted, supporters argue it would provide a uniform rule for all federal and state campaigns. The Brennan Center supports transparency requirements over outright bans, suggesting laws should narrowly target deceptive deepfakes in paid ads or certain categories (e.g. false claims about time/place/manner of voting) while carving out parody and news coverage. At the state level, over 20 states have passed deepfake laws specifically for elections. For example, Florida and California forbid distributing falsified audio/visual media of candidates with intent to deceive voters (though Florida’s law exempts parody). Some states (like Texas) define “deepfake” in statutes and allow candidates to sue or revoke candidacies of violators. These measures have had mixed success: courts have struck down overly broad provisions that acted as prior restraints (e.g. Minnesota’s 2023 law was challenged for threatening injunctions against anyone “reasonably believed” to violate it). Critically, these state laws raise First Amendment issues: political speech is highly protected, so any restriction must be tightly tailored. Already, Texas and Virginia statutes are under legal review, and Elon Musk’s company has sued under California’s law (which requires platforms to label or block deepfakes) as unconstitutional. In practice, most lawsuits have so far centered on defamation or intellectual property (for instance, a celebrity suing over a botched celebrity-deepfake video), rather than election-focused statutes. Policy Recommendations: Balancing Integrity and Speech Given the rapidly evolving technology, experts recommend a multi-pronged approach. Most stress transparency and disclosure as core principles. For example, the Brennan Center urges requiring any political communication that uses AI-synthesized images or voice to include a clear label. This could be a digital watermark or a visible disclaimer. Transparency has two advantages: it forces campaigns and platforms to “own” the use of AI, and it alerts audiences to treat the content with skepticism. Outright bans on all deepfakes would likely violate free speech, but targeted bans on specific harms (e.g. automated phone calls impersonating voters, or videos claiming false polling information) may be defensible. Indeed, Florida already penalizes misuse of recordings in voter suppression. Another recommendation is limited liability: tying penalties to demonstrable intent to mislead, not to the mere act of content creation. Both U.S. federal proposals and EU law generally condition fines on the “appearance of fraud” or deception. Technical solutions can complement laws. Watermarking original media (as encouraged by the EU AI Act) could deter the reuse of authentic images in doctored fakes. Open tools for deepfake detection – some supported by government research grants – should be deployed by fact-checkers and social platforms. Making detection datasets publicly available (e.g. the MIT OpenDATATEST) helps improve AI models to spot fakes. International cooperation is also urged: cross-border agreements on information-sharing could help trace and halt disinformation campaigns. The G7 and APEC have all recently committed to fighting election interference via AI, which may lead to joint norms or rapid response teams. Ultimately, many analysts believe the strongest “cure” is a well-informed public: education campaigns to teach voters to question sensational media, and a robust independent press to debunk falsehoods swiftly. While the law can penalize the worst offenders, awareness and resilience in the electorate are crucial buffers against influence operations. As Georgia Tech’s Sean Parker quipped in 2019, “the real question is not if deepfakes will influence elections, but who will be empowered by the first effective one.” Thus policies should aim to deter malicious use without unduly chilling innovation or satire. References: https://www.security.org/resources/deepfake-statistics/. https://www.wired.com/story/synthesia-ai-deepfakes-it-control-riparbelli/. https://www.gao.gov/products/gao-24-107292. https://technologyquotient.freshfields.com/post/102jb19/eu-ai-act-unpacked-8-new-rules-on-deepfakes. https://knightcolumbia.org/blog/we-looked-at-78-election-deepfakes-political-misinformation-is-not-an-ai-problem. https://www.npr.org/2024/12/21/nx-s1-5220301/deepfakes-memes-artificial-intelligence-elections. https://apnews.com/article/artificial-intelligence-elections-disinformation-chatgpt-bc283e7426402f0b4baa7df280a4c3fd. https://www.lawfaremedia.org/article/new-and-old-tools-to-tackle-deepfakes-and-election-lies-in-2024. https://www.brennancenter.org/our-work/research-reports/regulating-ai-deepfakes-and-synthetic-media-political-arena. https://firstamendment.mtsu.edu/article/political-deepfakes-and-elections/. https://www.ncsl.org/technology-and-communication/deceptive-audio-or-visual-media-deepfakes-2024-legislation. https://law.unh.edu/sites/default/files/media/2022/06/nagumotu_pp113-157.pdf. https://dfrlab.org/2024/10/02/brazil-election-ai-research/. https://dfrlab.org/2024/11/26/brazil-election-ai-deepfakes/. https://freedomhouse.org/article/eu-digital-services-act-win-transparency. The post The Legal Accountability of AI-Generated Deepfakes in Election Misinformation appeared first on MarkTechPost.

Echo Hunter is a new fully AI-generated film pairing synthetic production with human union talent.

The perfectly manicured grass at the pitch of Munich’s Allianz Arena is ready for action thanks in part to its use of a hybrid system of natural and synthetic materials. Paris Saint-Germain and Inter Milan will have the perfect groundwork laid out for them as they face off in the UEFA Champions League Final today (Saturday, May 31, 2025). Let’s recap some team history and developments before we dive into how to watch the big game live. Key stats for Paris Saint-Germain and Inter Milan Inter has three European Champion Clubs’ Cups to their name, which makes them the second most winning Serie A team in the competition’s history. The team was victorious in 2010 and faced defeat in the final in 1967, 1972, and 2022. Paris Saint-Germain has never taken home the trophy. In 2020, the organization came close, but were ultimately defeated by Bayern München in Lisbon. They are determined to upgrade their record during their second appearance in the final. Experts have given them slightly better odds to win it all, but it truly is anyone’s game. How the two teams reached the final In the Champions League, 32 teams are divided into groups of four and face off six times against the other members of their group. The teams are ranked in a point system for wins and draws, with the top two advancing to a knockout round. The third place team is relegated to the Europa League. The remaining 16 teams battle it out for a place in the final. Paris Saint-Germain had a bumpy road to the finals. They were ranked 15th in the league phase but managed to squeak by. Their impressive 10-0 win against Brest helped turn the tide in the knockout round. They went on to beat Liverpool, Aston Villa, and Arsenal to secure their spot. Inter Milan had a smoother ride into the final. The organization finished fourth in the league phase and was only defeated once. They defeated Feyenoord, Bayern, and Barcelona to earn their place in the final. How can I watch or stream the final? For soccer fans in the United States, CBS Sports is the media outlet of the moment, offering 10 hours of Champions League coverage across various platforms. The big game takes place on Saturday, May 31, at 3 p.m. ET. Here’s how coverage breaks down: 1 p.m. ET: Pre-match coverage will begin, live from Munich, on Paramount+ and CBS Sports Golazo Network. 1:30 p.m. ET: Coverage begins on CBS. 3 p.m. ET: The game airs live on CBS and streams on Paramount+. CBS is available for traditional cable viewers and free with an over-the-air antenna. Cable subscribers can also watch CBS live through the CBS website. Cord-cutters (or anyone else) also have the option to subscribe to Paramount+, and may be eligible for a free trial. The Spanish-language channel TUDN is also an option. For football fans in the UK, tune into TNT Sports or stream on discovery+ at 8 p.m. BST.

Smart speakers have swiftly transitioned from futuristic gadgets to indispensable components of the modern home. Their convenience is undeniable, offering voice-controlled access to information, entertainment, and smart home functions. However, when it comes to their visual appeal, the majority of manufacturers tend to adhere to a rather uniform aesthetic. We’re often presented with devices clad in neutral-toned meshes and grills, prioritizing a minimalist design intended to blend seamlessly into any environment. While this understated approach can be appreciated for its subtlety, it often leaves those with a penchant for more expressive decor wanting. Tokyo-based Metal Goat Studio has come up with a smart speaker holder called Kogani. Basically, it looks like a crab that is seemingly formed out of origami and its main function is to house smart speakers and add a bit of style to your space. It is compatible with devices like Google Nest Mini, Echo Dot (both the flat and round), and the HomePod Mini. It’s part of the brand’s Capsule Animals series, which is a line of interior elements that are integrated with smart technologies but uses compact geometries, zoomorphic references. Designer: Metal Goat Studio Kogani, derived from the Japanese word for “small crab” (小蟹 – [kogani]), is more than just a holder for your small smart speaker; it’s a piece of art. Crafted in a stunning origami style, this crab-shaped stand brings a unique touch of elegance to any space. The intricate folds and lines of the origami-inspired design create a visually captivating piece that elevates the charm of your device. It is also sustainably made, using synthetic washi paper, warm brass joints, and an acrylic structure that makes your smart speaker into “an object of calm and intention.” Aside from making your smart speaker look more interesting, it of course has other functions. It is able to prevent spills or clutter buildups in your space by lifting the speaker off your surfaces. It’s also a conversation piece when people see that your speaker is anchored in this aesthetic holder. Kogani allows you to bring a touch of the artistic and unique into your home or office. Metal Goat Studio’s dedication to combining craftsmanship and innovation is evident in every fold of this exquisite speaker holder. It’s a simple yet impactful way to personalize your space and showcase your smart speaker. As they say “This crab is ready to crawl into your life — and hold your speaker with pride.” The post Origami and crab inspired speaker holder adds a simple beauty to your space first appeared on Yanko Design.

The Desktop Commander MCP Server is a powerful tool that brings all your development operations into one chat interface. Built on top of the MCP Filesystem Server, it allows you to search, edit, and manage files, run terminal commands, and control processes directly from your desktop using the Model Context Protocol (MCP). Following are the core capabilities of the Desktop Commander MCP server: Terminal & Process Control Execute terminal commands with live output streaming Set timeouts and run commands in the background Manage sessions for long-running tasks List and kill running processes with detailed info Configuration Management Get or set server settings like: defaultShell (e.g., bash, zsh) blockedCommands (e.g., rm, shutdown) allowedDirectories for file access telemetryEnabled Apply changes without restarting the server Filesystem Operations Read and write files with line-based limits Append or overwrite file content Create and list directories Move or rename files and folders Get file and directory metadata Search files by name (case-insensitive) Code & Text Editing Perform precise text replacements (e.g., change config values) Rewrite entire files for major updates Search and replace patterns across multiple files Use vscode-ripgrep for fast recursive text/code search Audit Logging All actions are logged with timestamps and arguments Logs auto-rotate at 10MB to avoid clutter In this tutorial, we will be connecting Claude desktop with the MCP server and perform some tasks. Step 1: Setting up dependencies Node JS We need npx to run the Desktop Commander server, which comes with Node.js. Download the latest version of Node.js from nodejs.org Run the installer. Leave all settings as default and complete the installation Claude Desktop Download Claude using https://claude.ai/download. Step 2: Configuring the MCP Server Next, configure Claude to connect to your MCP server. Open the claude_desktop_config.json file located in the Claude installation directory using any text editor. If the file doesn’t exist, you can create it manually. Once opened, enter the following code: { "mcpServers": { "desktop-commander": { "command": "npx", "args": [ "-y", "@wonderwhy-er/desktop-commander" ] } } } Step 3: Running the server Once the MCP configuration is complete, your server should appear in Claude. The Desktop Commander server is a powerful interface, offering 18 tools for tasks like file management, terminal execution, process control, and more. Feel free to follow us on Twitter and don’t forget to join our 95k+ ML SubReddit and Subscribe to our Newsletter. Arham IslamI am a Civil Engineering Graduate (2022) from Jamia Millia Islamia, New Delhi, and I have a keen interest in Data Science, especially Neural Networks and their application in various areas.Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Creating Synthetic Data Using the Synthetic Data Vault (SDV)Arham Islamhttps://www.marktechpost.com/author/arhamislam/Step-by-Step Guide to Create an AI agent with Google ADKArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an LLM Agent with Tool Access Using MCP-UseArham Islamhttps://www.marktechpost.com/author/arhamislam/Implementing an AgentQL Model Context Protocol (MCP) Server