Last year, MACK Books published Architecture from Below, which anthologized writings by the French Brazilian architect, theorist, and painter Sérgio Ferro. (Douglas Spencer reviewed it for AN.) Now, MACK follows with Design and the Building Site and Complementary Essays, the second in the trilogy of books dedicated to Ferro’s scholarship. The following excerpt of the author’s 2023 preface to the English edition, which preserves its British phrasing, captures Ferro’s realization about the working conditions of construction sites in Brasília. The sentiment is likely relatable even today for young architects as they discover how drawings become buildings. Design and the Building Site and Complementary Essays will be released on May 22. If I remember correctly, it was in 1958 or 1959, when Rodrigo and I were second- or third year architecture students at FAUUSP, that my father, the real estate developer Armando Simone Pereira, commissioned us to design two large office buildings and eleven shops in Brasilia, which was then under construction. Of course, we were not adequately prepared for such an undertaking. Fortunately, Oscar Niemeyer and his team, who were responsible for overseeing the construction of the capital, had drawn up a detailed document determining the essential characteristics of all the private sector buildings. We followed these prescriptions to the letter, which saved us from disaster. Nowadays, it is hard to imagine the degree to which the construction of Brasilia inspired enthusiasm and professional pride in the country’s architects. And in the national imagination, the city’s establishment in the supposedly unpopulated hinterland evoked a re-founding of Brazil. Up until that point, the occupation of our immense territory had been reduced to a collection of arborescent communication routes, generally converging upon some river, following it up to the Atlantic Ocean. Through its ports, agricultural or extractive commodities produced by enslaved peoples or their substitutes passed towards the metropolises; goods were exchanged in the metropolises for more elaborate products, which took the opposite route. Our national identity was summed up in a few symbols, such as the anthem or the flag, and this scattering of paths pointing overseas. Brasilia would radically change this situation, or so we believed. It would create a central hub where the internal communication routes could converge, linking together hithertoseparate junctions, stimulating trade and economic progress in the country’s interior. It was as if, for the first time, we were taking care of ourselves. At the nucleus of this centripetal movement, architecture would embody the renaissance. And at the naval of the nucleus, the symbolic mandala of this utopia: the cathedral. Rodrigo and I got caught up in the euphoria. And perhaps more so than our colleagues, because we were taking part in the adventure with ‘our’ designs. The reality was very different — but we did not know that yet. At that time, architects in Brazil were responsible for verifying that the construction was in line with the design. We had already monitored some of our first building sites. But the construction company in charge of them, Osmar Souza e Silva’s CENPLA, specialized in the building sites of modernist architects from the so-called Escola Paulista led by Vilanova Artigas (which we aspired to be a part of, like the pretentious students we were). Osmar was very attentive to his clients and his workers, who formed a supportive and helpful team. He was even more careful with us, because he knew how inexperienced we were. I believe that the CENPLA was particularly important in São Paulo modernism: with its congeniality, it facilitated experimentation, but for the same reason, it deceived novices like us about the reality of other building sites. Consequently, Rodrigo and I travelled to Brasilia several times to check that the constructions followed ‘our’ designs and to resolve any issues. From the very first trip, our little bubble burst. Our building sites, like all the others in the future capital, bore no relation to Osmar’s. They were more like a branch of hell. A huge, muddy wasteland, in which a few cranes, pile drivers, tractors, and excavators dotted the mound of scaffolding occupied by thousands of skinny, seemingly exhausted wretches, who were nevertheless driven on by the shouts of master builders and foremen, in turn pressured by the imminence of the fateful inauguration date. Surrounding or huddled underneath the marquees of buildings under construction, entire families, equally skeletal and ragged, were waiting for some accident or death to open up a vacancy. In contact only with the master builders, and under close surveillance so we would not speak to the workers, we were not allowed to see what comrades who had worked on these sites later told us in prison: suicide abounded; escape was known to be futile in the unpopulated surroundings with no viable roads; fatal accidents were often caused by weakness due to chronic diarrhoea, brought on by rotten food that came from far away; outright theft took place in the calculation of wages and expenses in the contractor’s grocery store; camps were surrounded by law enforcement. I repeat this anecdote yet again not to invoke the benevolence of potential readers, but rather to point out the conditions that, in my opinion, allowed two students (Flávio Império joined us a little later) still in their professional infancy to quickly adopt positions that were contrary to the usual stance of architects. As the project was more Oscar Niemeyer’s than it was our own, we did not have the same emotional attachment that is understandably engendered between real authors and their designs. We had not yet been imbued with the charm and aura of the métier. And the only building sites we had visited thus far, Osmar’s, were incomparable to those we discovered in Brasilia. In short, our youthfulness and unpreparedness up against an unbearable situation made us react almost immediately to the profession’s satisfied doxa. Unprepared and young perhaps, but already with Marx by our side. Rodrigo and I joined the student cell of the Brazilian Communist Party during our first year at university. In itself, this did not help us much: the Party’s Marxism, revised in the interests of the USSR, was pitiful. Even high-level leaders rarely went beyond the first chapter of Capital. But at the end of the 1950s, the effervescence of the years to come was already nascent:  […] this extraordinary revival […] the rediscovery of Marxism and the great dialectical texts and traditions in the 1960s: an excitement that identifies a forgotten or repressed moment of the past as the new and subversive, and learns the dialectical grammar of a Hegel or an Adorno, a Marx or a Lukács, like a foreign language that has resources unavailable in our own. And what is more: the Chinese and Cuban revolutions, the war in Vietnam, guerrilla warfare of all kinds, national liberation movements, and a rare libertarian disposition in contemporary history, totally averse to fanaticism and respect for ideological apparatuses of (any) state or institution. Going against the grain was almost the norm. We were of course no more than contemporaries of our time. We were soon able to position ourselves from chapters 13, 14, and 15 of Capital, but only because we could constantly cross-reference Marx with our observations from well-contrasted building sites and do our own experimenting. As soon as we identified construction as manufacture, for example, thanks to the willingness and even encouragement of two friends and clients, Boris Fausto and Bernardo Issler, I was able to test both types of manufacture — organic and heterogeneous — on similar-sized projects taking place simultaneously, in order to find out which would be most convenient for the situation in Brazil, particularly in São Paulo. Despite the scientific shortcomings of these tests, they sufficed for us to select organic manufacture. Arquitetura Nova had defined its line of practice, studies, and research. There were other sources that were central to our theory and practice. Flávio Império was one of the founders of the Teatro de Arena, undoubtedly the vanguard of popular, militant theatre in Brazil. He won practically every set design award. He brought us his marvelous findings in spatial condensation and malleability, and in the creative diversion of techniques and material—appropriate devices for an underdeveloped country. This is what helped us pave the way to reformulating the reigning design paradigms.  We had to do what Flávio had done in the theatre: thoroughly rethink how to be an architect. Upend the perspective. The way we were taught was to start from a desired result; then others would take care of getting there, no matter how. We, on the other hand, set out to go down to the building site and accompany those carrying out the labor itself, those who actually build, the formally subsumed workers in manufacture who are increasingly deprived of the knowledge and know-how presupposed by this kind of subsumption. We should have been fostering the reconstitution of this knowledge and know-how—not so as to fulfil this assumption, but in order to reinvigorate the other side of this assumption according to Marx: the historical rebellion of the manufacture worker, especially the construction worker. We had to rekindle the demand that fueled this rebellion: total self-determination, and not just that of the manual operation as such. Our aim was above all political and ethical. Aesthetics only mattered by way of what it included—ethics. Instead of estética, we wrote est ética [this is ethics]. We wanted to make building sites into nests for the return of revolutionary syndicalism, which we ourselves had yet to discover. Sérgio Ferro, born in Brazil in 1938, studied architecture at FAUUSP, São Paulo. In the 1960s, he joined the Brazilian communist party and started, along with Rodrigo Lefevre and Flávio Império, the collective known as Arquitetura Nova. After being arrested by the military dictatorship that took power in Brazil in 1964, he moved to France as an exile. As a painter and a professor at the École Nationale Supérieure d’Architecture de Grenoble, where he founded the Dessin/Chantier laboratory, he engaged in extensive research which resulted in several publications, exhibitions, and awards in Brazil and in France, including the title of Chevalier des Arts et des Lettres in 1992. Following his retirement from teaching, Ferro continues to research, write, and paint.

Stranger Things fans are worried about the ultimate fate of main character Eleven, played by Millie Bobby Brown, and even some think the teen might not make it out alive at the end of the series.Eleven has been an integral part of the Duffer Brothers’ smash hit Netflix series since it first hit streaming in the summer of 2016.Viewers immediately gravitated toward the show for its spooky atmosphere and mystery-centered plot, nostalgic ’80s vibes and lovable cast of Goonies-esque teen characters.Fans have loved Eleven ever since she made her first appearance in Season 1, Episode 1, “The Vanishing of Will Byers,” and they've watched the unsure, traumatized and quiet young girl transform into a confident, spunky teen with powerful telekinetic abilities over the course of four seasons.Now though, with the series’ fifth and final season set to air later this year, longtime fans are worried about what the end of the show might spell for Eleven, now also known as Jane Hopper.Does Eleven Die in Stranger Things?Nothing about the fate of the core Stranger Things characters is known for sure at this time. However, that hasn’t stopped viewers from theorizing and speculating.During an appearance on U.K. talk show The Jonathan Ross Show in March 2024, Millie Bobby Brown may have inadvertently hinted that her character dies at the end of the show thanks to some questionable phrasing.While discussing the final season, the actress hinted, “I know how she ...” before catching herself and correcting, “I know what happens to my character.”The initial wording of “I know how she” sparked fans’ ears, many of whom thought the actress almost blurted out, “I know how she dies.”Brown also worried fans during a 2024 interview with Capital Radio, when she admitted she discovered her character’s fate after “kind of [forcing] myself into the writers’ room.”“I saw my ending and thought, ‘Oh,’ and then I walked away very slowly,” she cryptically teased.For years fans have speculated about the ending of Stranger Things, particularly about which of the core group might not make it out alive.Some fan theories suggest that Eleven is ultimately doomed, and might be forced to lock herself in the Upside Down forever to close the gate between the Upside Down and the real world, or will die heroically closing the gate and saving her friends and loved ones.Others believe Will Byers, who was the first to venture into the Upside Down and appears to still be connected to it as well as the series’ villain Vecna, will ultimately die in the finale.Of course, these are just fan theories. Hopefully, all the kids end up just fine and there's a big, happy ending!Stranger Things Season 5 will pick up after the epic events of Season 4, in which the kids learned about the evil Vecna, who ended the season by opening a hellish portal between the town of Hawkins and the Upside Down.The fifth season will be released in three parts: The first four episodes will hit Netflix on Nov. 26, three episodes will begin streaming on Dec. 25 and the series finale will air on Dec. 31.Sitcom Moments That Were Surprisingly DarkSitcoms such as The Simpsons and The Golden Girls are often seen as light-hearted comedies, but these darker TV moments offer a different, deeper perspective.Gallery Credit: Ryan ReichardGet our free mobile appREAD MORE: TV Shows Everyone Loves That Are Actually BadChild Stars Who Quit ActingStacker compiled this list of 25 child actors who quit show business, pulling from historical news coverage to include everyone from Mary-Kate and Ashley Olsen to Carrie Henn, who played the little girl in Aliens.Gallery Credit: Sophia June

In Is This Yours?,by going through four bins and finding their missing items.  The game itself is extremely simple, with the first world being at an airport’s lost and found. You see an animal looking for a specific item appear at the bottom of the screen. You need to read what they are looking for, then find the item in one of the four boxes, bringing the item to the designated place at the top of the screen. In all of the levels, the animals looking for their lost items do get frustrated quickly, so you need to find the items fast!  Once you’ve completed a level, you can go to the next. Each level of Is This Yours? has the items in different places, sometimes buried under other items, and sometimes boxes are even empty. The phrasing for the various items stays the same, so it’s easy to learn what you are looking for without reading it all, but the animals often ask for different items each time. If you aren’t a fan of racing against the timer while digging for stuff, there is a Zen mode where you are just whittling down the item to the final thing. You can easily finish Is This Yours? in one sitting. It’s a relaxing game that feels a little repetitive as you get to the end. I am also not sure if all of these items would be allowed in an airport (A sword?!). At the moment, the game only has the airport chapter, but there seems to be a second chapter in the works, as you can already see the levels laid out for this bit!  If you are looking for a simple game that you can finish in an afternoon and you want to sort out things, it’s got cute visuals and feels nice! Is This Yours? is available for free on iOS and Android. About The Author

Published May 27, 2025 10:00am EDT close Deepfake technology 'is getting so easy now': Cybersecurity expert Cybersecurity expert Morgan Wright breaks down the dangers of deepfake video technology on 'Unfiltered.' Imagine your phone rings and the voice on the other end sounds just like your boss, a close friend, or even a government official. They urgently ask for sensitive information, except it's not really them. It's a deepfake, powered by AI, and you're the target of a sophisticated scam. These kinds of attacks are happening right now, and they're getting more convincing every day.That's the warning sounded by the 2025 AI Security Report, unveiled at the RSA Conference (RSAC), one of the world's biggest gatherings for cybersecurity experts, companies, and law enforcement. The report details how criminals are harnessing artificial intelligence to impersonate people, automate scams, and attack security systems on a massive scale.From hijacked AI accounts and manipulated models to live video scams and data poisoning, the report paints a picture of a rapidly evolving threat landscape, one that's touching more lives than ever before. Illustration of cybersecurity risks. (Kurt "CyberGuy" Knutsson)AI tools are leaking sensitive dataOne of the biggest risks of using AI tools is what users accidentally share with them. A recent analysis by cybersecurity firm Check Point found that 1 in every 80 AI prompts includes high-risk data, and about 1 in 13 contains sensitive information that could expose users or organizations to security or compliance risks.This data can include passwords, internal business plans, client information, or proprietary code. When shared with AI tools that are not secured, this information can be logged, intercepted, or even leaked later.Deepfake scams are now real-time and multilingualAI-powered impersonation is getting more advanced every month. Criminals can now fake voices and faces convincingly in real time. In early 2024, a British engineering firm lost 20 million pounds after scammers used live deepfake video to impersonate company executives during a Zoom call. The attackers looked and sounded like trusted leaders and convinced an employee to transfer funds.Real-time video manipulation tools are now being sold on criminal forums. These tools can swap faces and mimic speech during video calls in multiple languages, making it easier for attackers to run scams across borders. Illustration of a person video conferencing on their laptop. (Kurt "CyberGuy" Knutsson)AI is running phishing and scam operations at scaleSocial engineering has always been a part of cybercrime. Now, AI is automating it. Attackers no longer need to speak a victim’s language, stay online constantly, or manually write convincing messages.Tools like GoMailPro use ChatGPT to create phishing and spam emails with perfect grammar and native-sounding tone. These messages are far more convincing than the sloppy scams of the past. GoMailPro can generate thousands of unique emails, each slightly different in language and urgency, which helps them slip past spam filters. It is actively marketed on underground forums for around $500 per month, making it widely accessible to bad actors.Another tool, the X137 Telegram Console, leverages Gemini AI to monitor and respond to chat messages automatically. It can impersonate customer support agents or known contacts, carrying out real-time conversations with multiple targets at once. The replies are uncensored, fast, and customized based on the victim’s responses, giving the illusion of a human behind the screen.AI is also powering large-scale sextortion scams. These are emails that falsely claim to have compromising videos or photos and demand payment to prevent them from being shared. Instead of using the same message repeatedly, scammers now rely on AI to rewrite the threat in dozens of ways. For example, a basic line like "Time is running out" might be reworded as "The hourglass is nearly empty for you," making the message feel more personal and urgent while also avoiding detection.By removing the need for language fluency and manual effort, these AI tools allow attackers to scale their phishing operations dramatically. Even inexperienced scammers can now run large, personalized campaigns with almost no effort. Stolen AI accounts are sold on the dark webWith AI tools becoming more popular, criminals are now targeting the accounts that use them. Hackers are stealing ChatGPT logins, OpenAI API keys, and other platform credentials to bypass usage limits and hide their identity. These accounts are often stolen through malware, phishing, or credential stuffing attacks. The stolen credentials are then sold in bulk on Telegram channels and underground forums. Some attackers are even using tools that can bypass multi-factor authentication and session-based security protections. These stolen accounts allow criminals to access powerful AI tools and use them for phishing, malware generation, and scam automation. Illustration of a person signing into their laptop. (Kurt "CyberGuy" Knutsson)Jailbreaking AI is now a common tacticCriminals are finding ways to bypass the safety rules built into AI models. On the dark web, attackers share techniques for jailbreaking AI so it will respond to requests that would normally be blocked. Common methods include:Telling the AI to pretend it is a fictional character that has no rules or limitationsPhrasing dangerous questions as academic or research-related scenariosAsking for technical instructions using less obvious wording so the request doesn’t get flaggedSome AI models can even be tricked into jailbreaking themselves. Attackers prompt the model to create input that causes it to override its own restrictions. This shows how AI systems can be manipulated in unexpected and dangerous ways.AI-generated malware is entering the mainstreamAI is now being used to build malware, phishing kits, ransomware scripts, and more. Recently, a group called FunkSac was identified as the leading ransomware gang using AI. Its leader admitted that at least 20% of their attacks are powered by AI. FunkSec has also used AI to help launch attacks that flood websites or services with fake traffic, making them crash or go offline. These are known as denial-of-service attacks. The group even created its own AI-powered chatbot to promote its activities and communicate with victims on its public website..Some cybercriminals are even using AI to help with marketing and data analysis after an attack. One tool called Rhadamanthys Stealer 0.7 claimed to use AI for "text recognition" to sound more advanced, but researchers later found it was using older technology instead. This shows how attackers use AI buzzwords to make their tools seem more advanced or trustworthy to buyers.Other tools are more advanced. One example is DarkGPT, a chatbot built specifically to sort through huge databases of stolen information. After a successful attack, scammers often end up with logs full of usernames, passwords, and other private details. Instead of sifting through this data manually, they use AI to quickly find valuable accounts they can break into, sell, or use for more targeted attacks like ransomware.Get a free scan to find out if your personal information is already out on the web Poisoned AI models are spreading misinformationSometimes, attackers do not need to hack an AI system. Instead, they trick it by feeding it false or misleading information. This tactic is called AI poisoning, and it can cause the AI to give biased, harmful, or completely inaccurate answers. There are two main ways this happens:Training poisoning: Attackers sneak false or harmful data into the model during developmentRetrieval poisoning: Misleading content online gets planted, which the AI later picks up when generating answersIn 2024, attackers uploaded 100 tampered AI models to the open-source platform Hugging Face. These poisoned models looked like helpful tools, but when people used them, they could spread false information or output malicious code.A large-scale example came from a Russian propaganda group called Pravda, which published more than 3.6 million fake articles online. These articles were designed to trick AI chatbots into repeating their messages. In tests, researchers found that major AI systems echoed these false claims about 33% of the time. Illustration of a hacker at work (Kurt "CyberGuy" Knutsson)How to protect yourself from AI-driven cyber threatsAI-powered cybercrime blends realism, speed, and scale. These scams are not just harder to detect. They are also easier to launch. Here’s how to stay protected:1) Avoid entering sensitive data into public AI tools: Never share passwords, personal details, or confidential business information in any AI chat, even if it seems private. These inputs can sometimes be logged or misused.2) Use strong antivirus software: AI-generated phishing emails and malware can slip past outdated security tools. The best way to safeguard yourself from malicious links that install malware, potentially accessing your private information, is to have strong antivirus software installed on all your devices. This protection can also alert you to phishing emails and ransomware scams, keeping your personal information and digital assets safe. Get my picks for the best 2025 antivirus protection winners for your Windows, Mac, Android & iOS devices.3) Turn on two-factor authentication (2FA): 2FA adds an extra layer of protection to your accounts, including AI platforms. It makes it much harder for attackers to break in using stolen passwords.4) Be extra cautious with unexpected video calls or voice messages: If something feels off, even if the person seems familiar, verify before taking action. Deepfake audio and video can sound and look very real.5) Use a personal data removal service: With AI-powered scams and deepfake attacks on the rise, criminals are increasingly relying on publicly available personal information to craft convincing impersonations or target victims with personalized phishing. By using a reputable personal data removal service, you can reduce your digital footprint on data broker sites and public databases. This makes it much harder for scammers to gather the details they need to convincingly mimic your identity or launch targeted AI-driven attacks.While no service can guarantee the complete removal of your data from the internet, a data removal service is really a smart choice.  They aren’t cheap - and neither is your privacy.  These services do all the work for you by actively monitoring and systematically erasing your personal information from hundreds of websites.  It’s what gives me peace of mind and has proven to be the most effective way to erase your personal data from the internet.  By limiting the information available, you reduce the risk of scammers cross-referencing data from breaches with information they might find on the dark web, making it harder for them to target you. Check out my top picks for data removal services here. 6) Consider identity theft protection: If your data is leaked through a scam, early detection is key. Identity protection services can monitor your information and alert you to suspicious activity. Identity Theft companies can monitor personal information like your Social Security Number (SSN), phone number, and email address, and alert you if it is being sold on the dark web or being used to open an account.  They can also assist you in freezing your bank and credit card accounts to prevent further unauthorized use by criminals. See my tips and best picks on how to protect yourself from identity theft.7) Regularly monitor your financial accounts: AI-generated phishing, malware, and account takeover attacks are now more sophisticated and widespread than ever, as highlighted in the 2025 AI Security Report. By frequently reviewing your bank and credit card statements for suspicious activity, you can catch unauthorized transactions early, often before major damage is done. Quick detection is crucial, especially since stolen credentials and financial information are now being traded and exploited at scale by cybercriminals using AI.8) Use a secure password manager: Stolen AI accounts and credential stuffing attacks are a growing threat, with hackers using automated tools to break into accounts and sell access on the dark web. A secure password manager helps you create and store strong, unique passwords for every account, making it far more difficult for attackers to compromise your logins, even if some of your information is leaked or targeted by AI-driven attacks. Get more details about my best expert-reviewed Password Managers of 2025 here.9) Keep your software updated: AI-generated malware and advanced phishing kits are designed to exploit vulnerabilities in outdated software. To stay ahead of these evolving threats, ensure all your devices, browsers, and applications are updated with the latest security patches. Regular updates close security gaps that AI-powered malware and cybercriminals are actively seeking to exploit. Kurt's key takeawaysCybercriminals are now using AI to power some of the most convincing and scalable attacks we’ve ever seen. From deepfake video calls and AI-generated phishing emails to stolen AI accounts and malware written by chatbots, these scams are becoming harder to detect and easier to launch. Attackers are even poisoning AI models with false information and creating fake tools that look legitimate but are designed to do harm. To stay safe, it’s more important than ever to use strong antivirus protection, enable multi-factor authentication, and avoid sharing sensitive data with AI tools you do not fully trust.Have you noticed AI scams getting more convincing? Let us know your experience or questions by writing us at Cyberguy.com/Contact. Your story could help someone else stay safe.For more of my tech tips & security alerts, subscribe to my free CyberGuy Report Newsletter by heading to Cyberguy.com/NewsletterAsk Kurt a question or let us know what stories you'd like us to coverFollow Kurt on his social channelsAnswers to the most asked CyberGuy questions:New from Kurt:Copyright 2025 CyberGuy.com.  All rights reserved. Kurt "CyberGuy" Knutsson is an award-winning tech journalist who has a deep love of technology, gear and gadgets that make life better with his contributions for Fox News & FOX Business beginning mornings on "FOX & Friends." Got a tech question? Get Kurt’s free CyberGuy Newsletter, share your voice, a story idea or comment at CyberGuy.com.

While large reasoning models (LRMs) have shown impressive capabilities in short-context reasoning through reinforcement learning (RL), these gains do not generalize well to long-context scenarios. Applications such as multi-document QA, research synthesis, and legal or financial analysis require models to process and reason over sequences exceeding 100K tokens. However, RL optimization in such regimes is plagued by slower reward convergence, unstable policy updates due to KL divergence fluctuations, and reduced exploration resulting from entropy collapse. These bottlenecks reveal a fundamental gap in transitioning LRMs from short-context proficiency to long-context generalization. QwenLong-L1: A Structured RL Framework for Long-Context Adaptation To address these limitations, the Qwen Research team introduces QwenLong-L1, a novel RL framework designed to adapt LRMs to long-context reasoning tasks. The framework is structured into three key stages: Warm-up Supervised Fine-Tuning (SFT): Provides a stable initialization for the policy model by training on curated question-context-answer triplets, ensuring basic competence in contextual comprehension and answer extraction. Curriculum-Guided Phased Reinforcement Learning: Introduces a staged training process with gradually increasing context lengths. This progression enables the model to incrementally acquire long-context reasoning behaviors without destabilizing policy updates. Difficulty-Aware Retrospective Sampling: Enhances exploration by maintaining and reusing hard examples from previous phases, weighted by their difficulty, to encourage deeper reasoning and robustness across diverse inputs. These stages are complemented by hybrid reward mechanisms—combining rule-based exact match verification with semantic evaluation by a lightweight LLM—ensuring both precision and recall during policy training. Technical Design and Methodological Advantages QwenLong-L1 integrates recent advances in group-relative RL optimization, specifically GRPO and DAPO, to mitigate the computational overhead associated with long-context value estimation: GRPO estimates advantage by normalizing rewards within sampled groups, eliminating the need for a separate value network and encouraging diverse generation patterns. DAPO incorporates mechanisms such as dynamic sampling, overlength penalty shaping, and asymmetric clipping thresholds to prevent entropy collapse and mitigate length biases during training. The reward function is defined as the maximum of two signals: a deterministic rule-based match and a semantic judgment from a compact evaluator model (e.g., Qwen2.5-1.5B). This hybrid approach avoids overfitting to rigid formats while maintaining answer correctness across varied notations and phrasings. Moreover, the framework is optimized via progressive context scaling, where the RL process transitions from 20K-token to 60K-token input lengths in controlled phases, stabilizing training dynamics and facilitating policy generalization. Experimental Results and Benchmark Performance QwenLong-L1 was evaluated on seven long-context document QA benchmarks, including DocMath, Frames, 2WikiMultihopQA, HotpotQA, Musique, NarrativeQA, and Qasper. The 32B variant, QwenLong-L1-32B, demonstrated strong empirical performance: It outperformed baseline models such as R1-Distill-Qwen-32B by 5.1 points and exceeded leading proprietary systems like OpenAI-o3-mini and Qwen3-235B-A22B. Its performance was comparable to Claude-3.7-Sonnet-Thinking, indicating competitive reasoning capabilities under extreme context lengths. Pass@K analysis revealed consistent improvements with increased sampling, achieving a Pass@2 average of 73.7, surpassing DeepSeek-R1 and OpenAI-o1-preview, even at low sampling rates. Ablation studies further validated the individual contributions of SFT, phased RL, and retrospective sampling. Notably, RL played a decisive role in enabling emergent reasoning behaviors such as grounding, subgoal setting, verification, and backtracking—traits not effectively induced by supervised fine-tuning alone. Conclusion QwenLong-L1 represents a systematic approach to equipping LRMs with robust long-context reasoning capabilities through reinforcement learning. Its design effectively bridges the gap between short-context expertise and the demands of information-dense environments by combining supervised initialization, curriculum-driven context scaling, and hybrid evaluation strategies. The framework not only achieves state-of-the-art results across long-context benchmarks but also demonstrates the emergence of interpretable reasoning patterns during training. Check out the Paper, Model on Hugging Face and GitHub 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. Asif RazzaqWebsite |  + postsBioAsif Razzaq is the CEO of Marktechpost Media Inc.. As a visionary entrepreneur and engineer, Asif is committed to harnessing the potential of Artificial Intelligence for social good. His most recent endeavor is the launch of an Artificial Intelligence Media Platform, Marktechpost, which stands out for its in-depth coverage of machine learning and deep learning news that is both technically sound and easily understandable by a wide audience. The platform boasts of over 2 million monthly views, illustrating its popularity among audiences.Asif Razzaqhttps://www.marktechpost.com/author/6flvq/NVIDIA Releases Llama Nemotron Nano 4B: An Efficient Open Reasoning Model Optimized for Edge AI and Scientific TasksAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Coding Implementation to Build an AI Agent with Live Python Execution and Automated ValidationAsif Razzaqhttps://www.marktechpost.com/author/6flvq/Step-by-Step Guide to Build a Customizable Multi-Tool AI Agent with LangGraph and Claude for Dynamic Agent CreationAsif Razzaqhttps://www.marktechpost.com/author/6flvq/A Comprehensive Coding Guide to Crafting Advanced Round-Robin Multi-Agent Workflows with Microsoft AutoGen

Every generation has a small group of young fiction writers who make it: They top bestseller lists, win prizes, and become household names. And for decades — well, nearly every decade — they have all been straight white men. Philip Roth. Norman Mailer. John Updike. Jonathan Franzen. Jonathan Safran Foer. You get the picture. But in the last decade or so, that’s changed: The up-and-coming writers capturing buzz and dominating critics’ lists have largely been women. Think Sally Rooney or Emma Cline or Ottessa Moshfegh. And when men do break through, they usually aren’t young, straight, or white. It’s worth pointing out that, while women now publish more books than men, men are still publishing more books now than they ever have before.But the (relative) decline of the men in letters has led to searching discussions, first murmured, but now increasingly debated in places like the New York Times and the Guardian: Why does the decline of the young, white, male writer matter? And what do we lose — if anything — with this shift?“We’ve seen a lot of great work being done to account for perspectives that were left out of literature for a long time,” Ross Barkan, a journalist and novelist, told Today, Explained co-host Noel King. “But I also think it’s important to know, for better and for worse, what the men of the 2020s are up to.”Barkan and King talked about how he feels young men have been shut out of literary fiction, what he thinks is lost, and his experience trying to get fiction published. His third novel, Glass Century, was released earlier this month.Below is a transcript of their conversation, edited for length and clarity. Make sure to listen to hear the whole thing wherever you get podcasts, including Apple Podcasts and Spotify. We are talking to you today because you wrote an essay not long ago called “From Misogyny to No Man’s Land: The Vanishing Male in Contemporary Literature.” What’s your argument in that essay, Ross?My argument in that essay is that among young literary writers today, there is a lack of men. This doesn’t mean there are no male novelists of prominence under the age of 40 — that’s the cutoff I use for young — but there are fewer of them than there were historically. And most of the prominent literary fiction writers today are women. I’m talking about a very specific type of fiction that is vying for awards or trying to vie for awards, trying to attain a certain level of prestige.You’re 35, and you’re a white man? Correct. I wonder about the kind of driving force for this essay and whether you are the vanishing male writer of which you wrote.I think so, yeah, I think there’s less of me for sure. I mean, there’d be an era where there were a lot of novelists like myself, Jewish or not Jewish, but certainly white men.I am inclined to find your argument very compelling. I was a teenager in the ’90s, a young adult in the 2000s. That’s when you read a lot of fiction, right? And I do remember David Foster Wallace, Jonathan Safran Foer…Yeah.…Jonathan Franzen. Jonathan Lethem! And so what you’re saying actually really does track to me. The question I wonder about is the why. And let me ask you first to answer the why from your personal perspective. You’re a novelist. You’re 35 years old. You’re a straight white guy — do you feel like those identities are holding you back in some way?Not in the real world. In the real world, I have enormous privilege. But in the 2010s, the literary world was less interested in straight men. I think you have a general lack of the heterosexual male perspective in newer fiction. There’s a long history of writers portraying toxic masculinity and rough male characters — and it feels like you see less of that today. I also think at the same time, young male writers, white and non-white, were taking less of an interest in fiction. It’s a chicken-and-egg challenge: Is it the publishing industry deciding this is no longer something we’re going to push or take a real interest in, or is it market forces as well?So some of it is internal — maybe there are fewer men who want to be great novelists, but maybe publishers are saying, “Hey, we’re just less interested in the perspectives of straight white men.” When you approached publishers with your novel Glass Century, did you hear that?I think you hear it behind the scenes. You’re never told to your face. I’m not complaining — I don’t consider myself a victim. I’ve had a successful career. I’m very happy with it. But what do you hear behind the scenes?To echo Joyce Carol Oates in a sort of notorious but not wrong tweet from several years ago — and I’m paraphrasing — agents and editors, at least in the 2010s and early 2020s, were just less interested in straight male fiction. I want to broaden it a little bit because you see even among Black, Hispanic, and Asian straight men — there are some, but [they’re] less common. And, certainly, the white male is now even less common, so I think publishers in general in that era were trying to diversify, which was fine. You had social justice politics, you had what they call “woke,” and in a way woke worked because it broadened things out and brought in new voices, but it is also zero sum. Some come up; some go out. And so for me, it’s observing that trend. What do you think we lose when we lose the perspective of those young white men?It’s a large part of the country. I think you have a lot going on with young men today. White and non-white alike, straight men — they are falling behind academically. They’re increasingly alienated. They’re increasingly angry. They are increasingly online. And fiction, in my view, is not grappling with all of that.I agree with you, but I did actually see that in one book in the last year, Rejection by Tony Tulathimutte. There were characters who were highly online. The most acclaimed story was about an incel. That book was incredibly powerful. And it got praise, right? What do you think about that?He’s a fantastic writer. I’ll start there. He’s a great prose stylist. There’s a short story I love about a young Asian man who is having these very lurid sexual fantasies about dominating other men. Fantastically written — he’s sort of the Roth of our era in terms of his ability to make a sentence really sizzle. But this is the caveat that people seem to be afraid to point out: It’s not a straight male fantasy. Could Tony have written a straight male fantasy of wanting to subdue a woman the way that character wants to subdue men? Tony himself is straight. It was an interesting choice there to inhabit a gay character. Nothing wrong with that. Writers should write about whatever sexuality. I don’t believe in limiting anyone in that way. But I thought it was a choice, right? Because straight male lust is very disconcerting. It’s not easy to write about. What do men think about? The modern novel is not addressing that enough. The nasty, nasty men. The men who are not — maybe they’re good at heart, but they have a lot of bad thoughts. And they take bad actions. You don’t see that much in fiction today, I would argue.Let me ask you about an argument that I think many people might have in response to what you’ve said, including many women. If you look at the stats going back to the year 1800, women made up about 5 percent of published authors. It’s 10 percent through about the 1900s, and then in 2015, women surpassed men — more women are publishing books than men. Although both genders are still publishing a lot of books, it should be said.Are you at all sympathetic to the argument that you guys had your turn for centuries, the attention, the prizes, the accolades, so we’re just leveling the playing field out?Yeah, I’m sympathetic, for sure. I think that it’s reasonable to believe that — that’s an honest argument. The problem is you’ll hear from people who say this isn’t happening, and I find that very tiring. I think the honest thing to say is that it’s time to rebalance the scales or turn the tables. But there are winners and losers, right? Women were losing; now men are losing. I will say, there’s no solace offered to the 26-year-old male who must pay for the sins of the past, right? The young male writer can’t sit at home and think, Well, golly, it was good Norman Mailer and John Updike had such a great run. So yes, I think one can rebalance, one can seek balance, one can ensure that groups of people who are discriminated against have their time as they should. My point merely is that you can’t then pretend there aren’t those who aren’t getting what they want.See More:

Politics, you will notice, has gotten extremely weird.To some degree, of course, this is Donald Trump’s fault. No other president has seen the first part of their term defined by a fight over whether the federal government can send people living in the US to a prison in El Salvador with no due process. No other modern president has decided to ignore decades of settled economic and political wisdom and institute the biggest tariffs since the Hoover administration. No other president has waged war against the entire foundation of American science. Some weirdness is also the fault of Covid. The pandemic introduced a slew of policies that proved divisive, from mask mandates to vaccine mandates to funding for “gain of function” research to school closures. None of these were polarizing topics in 2019 because they either had never happened before or were too obscure for most people to care. And though we’re a few years past the worst days of the pandemic, the appointment of anti-vaxxer Robert F. Kennedy Jr. as health and human services secretary shows just how central many of these topics remain.It’s this context that has made Congress’s debate over a multitrillion-dollar reconciliation bill so fascinating. The bill’s contents are still evolving, but the broad outlines are simple: trillions in tax cuts, tilted to the wealthy; hundreds of billions in spending cuts, particularly to programs for the poor like Medicaid and food stamps; over a hundred billion dollars in increased spending for defense.I know of no better summary of its effects than the above chart from the Urban Institute, which shows that it would make poor Americans earning less than $10,000 dramatically worse off (reducing their income by 14.9 percent) while affluent households earning over $200,000 would thrive.So, all in all, a terrible bill. But whatever else that proposal is, it’s startlingly normal for Republican politics. It represents ideas that have defined the Republican party and its economic and budgetary priorities since 1980, and which the party has strongly held to even in the face of Trump’s total takeover. The Republican party stands for lower taxes, especially on the rich; lower spending on programs for the poor; and big spending on defense. That’s what Ronald Reagan, Newt Gingrich, Paul Ryan, and other figures who defined the party have all stood for, for nearly half a century now.The extreme weirdness of national politics has led to a temptation to see a new Republican party just over the horizon, defined by rejecting its tax-cutting and program-slashing tradition. This is stoked by strategic leaks that Trump might be open to a higher tax rate on the richest Americans; by Sen. Josh Hawley (R-MO) condemning Medicaid cuts; by party figures like Vice President JD Vance suggesting a break from the party’s hawkish foreign policy.But the composition of the reconciliation bill suggests that when it comes to bread-and-butter economic issues, this is mostly a mirage. The essential Republican message may become blurred around the edges, the way that George W. Bush messed with it by expanding Medicare or his father did by accepting a small tax hike. But the deviations are swamped by the continuity. It’s not, in the ludicrous phrasing of Steve Bannon, a “workers’ party.”Congressional Republicans, led by Speaker Mike Johnson, have brought back normal politics, and for them that means one thing: redistributing income upward.The rise and persistence of Normal RepublicanismThis essential pattern of Republicans standing for across-the-board tax cuts and cuts to safety net programs has not always been the norm. Nothing in politics is truly permanent. As late as Richard Nixon, Republican presidents would propose ideas like a guaranteed minimum income and universal health coverage while actually raising taxes on the rich.The ground shifted in the late 1970s and early 1980s, when a small group of policy entrepreneurs in Washington centered around Congress member Jack Kemp (R-NY) began promoting across-the-board cuts to individual income tax rates as a solution to stagflation (the combination of slow growth and high inflation then characterizing the economy). In her history of this moment, Starving the Beast, sociologist Monica Prasad notes that major business lobbies at the time opposed this move. Their priority was corporate rate cuts and a balanced budget, and they saw individual cuts as a threat to both.Kemp and his allies, including soon-to-be-President Reagan, overcame corporate skepticism for one simple reason: The cuts were popular, and the public mood was becoming strongly anti-tax. At this point in time, the thresholds for tax rates were not indexed for inflation, which meant that more and more middle-class people were being pushed into higher and higher tax brackets every year without actually becoming richer as inflation worsened. These pressures had forced even Democrat Jimmy Carter to sign tax cuts in 1978, and they only built as inflation rose still further.Timeline of major Republican tax billsTax Reform Act of 1969 — signed by Richard Nixon, cracked down on foundations, extended a temporary across-the-board income tax hike to fund the war in Vietnam, and created the Alternative Minimum Tax, meant to target high-earners claiming many deductions and credits. On net, substantially raised taxes on the rich while cutting them for the poor.Economic Recovery Tax Act of 1981 — signed by Ronald Reagan, across-the-board cut in tax rates for individuals, with top rate falling from 70 percent to 50 percent. Tax thresholds now indexed for inflation. Businesses allowed to deduct expenses at an accelerated pace.Tax Equity and Fiscal Responsibility Act of 1982 — also signed by Reagan, undid most of the 1981 cuts to corporate taxes, but crucially kept the cuts on individuals in place.Tax Reform Act of 1986 — bipartisan legislation signed by Reagan that eliminated many deductions and credits and simplified the individual income tax to only two brackets (15 percent and 28 percent).Omnibus Budget Reconciliation Act of 1990 — signed by George H.W. Bush, added a 31 percent bracket on the rich to raise revenue on top of the 1986 law. Bill Clinton would add 36 percent and 39.6 percent rates in 1993.Taxpayer Relief Act of 1997 — signed by Bill Clinton but championed by Republicans in Congress, created a $500 child tax credit and cut the capital gains rate from 28 percent to 20 percent.Economic Growth and Tax Relief Reconciliation Act of 2001 — signed by George W. Bush, slashed individual rates across the board, with the top rate falling from 39.6 percent to 35 percent, and gradually eliminated the estate tax. Initially set to expire in 2010. Extended temporarily by Barack Obama in 2010 as part of an economic stimulus deal, and then in 2012 permanently, but only for couples earning under $450,000.Jobs and Growth Tax Relief Reconciliation Act of 2003 — signed by George W. Bush, cut taxes on dividend and interest income, and limited the Alternative Minimum Tax’s effects. Set to expire in 2010, like the 2001 cuts; largely expired under the 2012 Obama deal.Tax Cuts and Jobs Act of 2017 — signed by Donald Trump, cut individual rates with the top rate falling from 39.6 percent to 37 percent; doubled the standard deduction and consolidated personal and dependent exemptions into a larger child tax credit; dramatically cut the corporate tax rate from 35 percent to 21 percent. While some cuts, like the corporate rate cut, were permanent, most of the bill is set to expire at the end of 2025.At the same time, Reagan and his team in the early 1980s were convinced that the US needed a major military buildout to counteract what they claimed had been a Soviet buildout in the 1970s. That led to a big increase in defense spending, from 6.6 percent of GDP in 1981 to 7.6 percent in 1985; at today’s size of the US economy, an equivalent increase would be about $290 billion more per year.To pay for at least some of this, Reagan’s first reconciliation bill included sweeping cuts to safety net programs, notably including Medicaid, food stamps, and Aid to Families with Dependent Children (AFDC). This, too, fit well with Reagan’s image. He had campaigned hard against establishing Medicare in the 1960s, and denunciations of “welfare queens” had been a prominent theme in his ultimately failed 1976 campaign for the presidency. This wasn’t the most popular part of the Reagan brand (he denounced “welfare queens” while trying to win the Republican primary, not the general), but it reflected both his genuine beliefs and the twin pressures of the tax cuts and defense buildout on the budget.This combination of policies was a profound break from the Nixon/Gerald Ford years, when tax cuts were not seriously considered, the priority with the Soviets was detente and arms control, and safety net programs were largely protected. And, sure enough, some Republicans continued to push back against the new regime. Sen. Bob Dole (R-KS) successfully fought to limit food stamps cuts; Congressional Republicans worked with Democrats to expand Medicaid throughout the 1980s over Reagan’s objections; most infamously, George H.W. Bush signed a bill adding a new 31 percent tax bracket for rich Americans in 1990, violating his pledge not to raise taxes.But for the most part, the pattern established by Reagan has persisted ever since, and deviations — like Bush’s tax hike, which contributed to his loss in 1992 — are remembered more as cautionary tales than examples to emulate.Look at the Contract With America, the Republicans’ platform during their successful 1994 bid to retake the House for the first time in over 40 years. It included tax cuts (like introducing a child tax credit and lower capital gains rates) and cuts to welfare and other safety net programs. While Bill Clinton was able to tamp down these demands somewhat, all became law in one form or another.A few years later, George W. Bush began his first term with sweeping across-the-board tax cuts, and his second with a failed effort to slash Social Security spending in favor of individual accounts. In the Obama years, Congressional Republicans, led by Paul Ryan, coalesced around plans for yet more across-the-board tax cuts and sweeping cuts to Medicare, Medicaid, and other safety net programs. Under Trump, Ryan was able to pass the former, though his attempts at the latter through Obamacare repeal failed.Occasionally, a Republican politician will gesture at trying to break with this orthodoxy, and is invariably greeted with intrigued attention from the press. With George W. Bush in 2000, it was called “compassionate conservatism.” With Minnesota Gov. Tim Pawlenty in 2005, it was “Sam’s Club Republicanism.” With then-Sen. Marco Rubio and his allies in the press circa 2014, it was “reform conservatism.” These days the preferred term for Sen. Josh Hawley and Vice President JD Vance seems to be “national conservative,” which, like the Sam’s Club and Reformocons before, purports to reject the tax-cutting orthodoxy of past Republicans in favor of a more communitarian vision — very little of which, of course, appears to have made its way into the budget bill. All of this has happened before, and all of this will happen again.A very Normal Republican budgetBut through each of these much-hyped fads, Normal Republicanism on the budget has survived more or less unchanged. The legacy of compassionate conservatism is a prescription drug benefit in Medicare administered by private insurers; the legacy of reform conservatism is mostly increasing the child tax credit from $1,000 to $2,000 in 2017. These are slight ripples in a pattern that has been remarkably persistent.The 2025 reconciliation package is a perfect illustration of these dynamics. Per a helpful tally by the Committee for a Responsible Federal Budget, the centerpiece of the legislation is the extension and expansion of Trump’s 2017 Tax Cuts and Jobs Act. The cuts here will cost over $4.1 trillion over a decade. Remarkably, some of the cuts aren’t even made permanent, but temporarily extended again, to artificially make the cost look lower than it is; if they’re extended still further, the total cost of the TCJA extensions would be more like $4.8 trillion.Deficits would surge dramatically due to the reconciliation bill. Committee for a Responsible Federal BudgetThese are tax cuts overwhelmingly tilted at the top. $1.4 trillion goes to repealing the Alternative Minimum Tax, which exists to prevent high-earners from taking excessive deductions; $2.2 trillion goes to cutting rates, including the top rate, which disproportionately helps the rich. The pass-through deduction, which arbitrarily lets some business owners exclude 20 percent of their profit from taxation, is extended and also expanded to 23 percent for no apparent reason, for a mere $820 billion. The Tax Policy Center has estimated that extending the TCJA increases incomes for the top 1 percent by an average of 3.7 percent, which swamps the 0.6 percent increase that the poorest fifth of Americans would get.On top of this, the House Ways and Means committee has thrown a potpourri of assorted other tax cuts: through 2028, for instance, tips, overtime income, and car loan interests would be tax-free, and senior citizens would get bigger standard deductions. The spending spree isn’t limited to taxes, either. There’s $144 billion from the Armed Services Committee, focused on shipbuilding ($32 billion) and “air superiority and missile defense” ($30 billion), and $67 billion for border security, including about $50 billion for Trump’s long-promised wall.The gross cost of all these giveaways hits around $5 trillion, before even considering the possibility that giveaways like the tips and overtime tax cuts are made permanent. But the net cost of the package, and impact on the defiict is “only” about $3.3 trillion, per the Committee for a Responsible Federal Budget. What makes up the difference? $698 billion comes from cuts to Medicaid; including other cuts to Affordable Care Act subsidies, the bill will deprive about 10 million people of health insurance. $559 billion comes from undoing most of the Inflation Reduction Act’s credits for clean energy and electric vehicles. $350 billion comes from cuts to education, heavily focused on student loan programs and subsidies meant to make them more affordable for borrowers. $267 billion comes from food stamps, slashing the program by about 30 percent overall.There’s a lot to say about this set of priorities. The Medicaid and food stamp cuts target the most vulnerable Americans and, combined with the tax cuts for the wealthy, amount to extreme upward redistribution. The Inflation Reduction Act cuts will likely substantially increase energy prices for most Americans, while substantially increasing emissions. I know of no serious economist who thinks that many of the most expensive provisions in the bill, like deductibility of overtime income or the pass-through business deduction, are effective ways to boost economic growth.But, at a moment where so many assumptions about politics have been overturned, the plan is not surprising. This is not a radically different Republican party newly attuned to the interests of the working class. It’s not a party whose tax-cutting passions have been tempered now that their president is imposing new taxes left and right on foreign imports in the form of tariffs (and which will be borne disproportionately by lower-income Americans). It’s not a party reflecting the fact that Medicaid recipients narrowly voted for Trump over Kamala Harris in 2024.It’s just the normal Reagan-Gingrich-Bush-Ryan Republican party, same as it’s ever been.Why, then, are people so eager to hear that the Republican party has changed? Part of it might be the simple fact that voters have had trouble accepting that a political party could actually be like this. When Priorities USA, a Democratic super-PAC in the 2012 election, told a focus group that Mitt Romney wanted to slash Medicare while cutting taxes on the rich, “the respondents simply refused to believe any politician would do such a thing,” per reporter Robert Draper. It does sound vaguely ridiculous: It defies common sense that cutting taxes on the wealthy and funding it with spending cuts on programs for poor and working people would ever be a compelling political message, perhaps outside the extreme inflationary environment of the 1970s that birthed Reagan’s presidency.But ridiculous or not, that’s the world we have. The Republican Party’s budgetary views simply have not changed. They want to blow up the deficit with massive tax cuts tilted toward the rich and pay for a fraction of the cost by slashing programs for the poor. It’s really that simple. It may not be exciting or brand new. But it’s normal Republican policymaking, and it’s back with a vengeance.See More:

Pankaj Khurana, VP Technology & Consulting, RocketMay 19, 20253 Min ReadElenaBs via Alamy StockFor the past four years, I’ve been building AI-powered tools that help recruiters do their job better. Before that, I was a recruiter myself -- reading resumes, making calls, living the grind. And here’s one thing I’ve learned from straddling both worlds: In hiring, automating the wrong thing can quietly erode everything that makes your process work. As engineering leaders, we’re constantly told to streamline and optimize. Move fast. But if you automate the wrong step -- like how candidates are filtered, scored, or messaged -- you might be replacing good human judgment with rigid shortcuts. And often, you won’t notice the damage until weeks later, when engagement plummets or teams stop trusting your system. The Allure of Automation Hiring is messy. Resumes come in all shapes. Job descriptions are vague. Recruiters are overworked. AI seems like a godsend. We start by automating outreach. Then scoring. Then matching. Eventually, someone asks: can this whole thing run without a person? But here’s the rub: many hiring decisions are deeply contextual. Should a product manager with a non-traditional background be fast-tracked for a high-growth SaaS role? That’s not a “yes/no” the system can decide for you. Early on at Rocket, we made that mistake. Our scoring engine prioritized resumes based solely on skills overlap. It was fast -- but completely off for roles that required nuance. We had to pause, rethink, and admit: “This isn’t working like we hoped.” Related:What Agentic Systems Do Well I’m not anti-automation. Far from it. But it has to be paired with human review. We found that agentic systems -- AI tools with autonomy to assist but not decide -- were far more effective. Think copilots, not autopilots. For example, our system can: Suggest better phrasing for job descriptions Flag resumes that match roles 80% or more Recommend outreach templates based on role and tone But it never auto-rejects or sends messages without review. The AI suggests; the recruiter decides. That balance makes all the difference. Lessons Learned: Where Automation Fails One of our biggest missteps? Automating outreach too heavily. We thought sending personalized AI-written emails at scale would boost response rates. It didn’t. Candidates sensed something off. The emails looked polished but felt cold. Engagement dropped. We eventually went back to having humans rewrite the AI drafts. That one shift nearly doubled our positive response rate. Why? Because candidates want to feel seen -- not sorted. Related:A CIO’s Checklist: What Not to Automate If you’re leading an AI initiative in hiring, here’s a checklist we now swear by: Don’t automate decisions that impact trust. Rejections, scores, hiring calls? Keep a human in the loop. Avoid automating tasks with high context needs. A great candidate might not use trendy buzzwords. That doesn’t make them a bad fit. Be careful with candidate-facing automation. Generic outreach harms brand perception. Do automate the repetitive stuff. Parsing, meeting scheduling, draft -- automate those and give time back to your team. Human-AI Collaboration Wins We saw the best outcomes when recruiters felt like they had an assistant -- not a competitor. Here’s one quick story: A recruiter used our AI to shortlist 10 profiles for a hard-to-fill GTM analyst role. She reviewed five, adjusted the messaging tone slightly, and got two responses in under a day. Same tools -- different mindset. Feedback loops mattered too. We built in ways for users to rate suggestions. The model kept improving -- and more importantly, people trusted it more. Final Thought: Think Like a System Designer If you’re building AI into your hiring stack, go beyond automation. Think augmentation. Don’t just ask, “Can this task be automated?” Instead, ask, “If I automate this, what do we lose in context, empathy, or nuance?” Related:Agentic hiring systems can deliver speed and scale -- but only if we let people stay in control of what matters most. About the AuthorPankaj KhuranaVP Technology & Consulting, RocketPankaj Khurana is VP of Technology & Consulting at Rocket, an AI-driven recruiting firm. He has over 20 years of experience in hiring and tech and has led the development of agentic hiring tools used by top US startups. See more from Pankaj KhuranaWebinarsMore WebinarsReportsMore ReportsNever Miss a Beat: Get a snapshot of the issues affecting the IT industry straight to your inbox.SIGN-UPYou May Also Like

As AI reshapes language, even the human voice becomes a pattern to be predicted, not a meaning to be ... More understood.getty Language is the foundation of business, culture, and consciousness. But AI isn’t just using our words—it’s reshaping them. Quietly, subtly, it’s dismantling the architecture of thought by eroding what we used to think: nouns. We used to believe that naming something gave it power. Giving a thing a noun means tethering it to meaning, identity, and memory. But in the age of AI, nouns are dissolving—not banned, not erased—but rendered functionally obsolete. And with them, our grasp on reality is starting to fray. AI and the Architecture of Thought AI doesn’t see the world in things. It sees the world in patterns—actions, probabilities, and prompts. A chair is no longer an object; it’s “something to sit on.” A self is no longer an identity; it’s “a collection of behaviors and preferences.” Even brands, once nouns wrapped in mythology, are being reconstituted as verbs. You don’t have a brand. You do a brand. This linguistic shift isn’t neutral. It’s a collapse of conceptual anchors. In generative systems, nouns aren’t centers of gravity. They’re scaffolding for action. This reflects a broader trend in how generative AI is reshaping communication across every industry. Recent research supports this trend. A study titled Playing with Words: Comparing the Vocabulary and Lexical Richness of ChatGPT and Humans found that ChatGPT’s outputs exhibit significantly lower lexical diversity than human writing. In particular, nouns and specific, stylistic words are often underused, suggesting that generative systems prioritize predictable, commonly used language while deprioritizing less frequent terms. Further analysis of 14 million PubMed abstracts revealed a measurable shift in word frequency post-AI adoption. Words like “delves” and “showcasing” surged, while others faded—showing that large language models are already reshaping vocabulary patterns at scale. Sound familiar? It should. AI’s Philosophical Ancestors: Orwell, Huxley, and the Future They Warned Us About To understand their relevance, it helps to recall what George Orwell and Aldous Huxley are most famous for. Orwell authored 1984, a bleak vision of the future where an authoritarian regime weaponizes language to suppress independent thought and rewrite history. His concept of Newspeak—a restricted, simplified language designed to make dissent unthinkable—has become a cultural shorthand for manipulative control. On the other hand, Huxley wrote Brave New World, which envisioned a society not characterized by overt oppression, but rather by engineered pleasure, distraction, and passive conformity. In his world, people are conditioned into compliance not through violence but through comfort, entertainment, and chemical sedation. Both men anticipated futures in which language and meaning are compromised, but in radically different ways. Together, they map the two poles of how reality can be reconditioned: by force or indulgence. Few realize that George Orwell was once a student of Aldous Huxley. In the late 1910s, while Orwell (then Eric Blair) studied at Eton, Huxley taught him French. Their relationship was brief but prophetic. Decades later, each would author the defining visions of dystopia—1984 and Brave New World. After reading 1984, Huxley wrote to Orwell with a haunting message: Whether in actual fact the policy of the boot-on-the-face can go on indefinitely seems doubtful… The future will be controlled by inflicting pleasure, not pain. And that’s precisely where we are now. Orwell feared control through surveillance and terror. Huxley feared control through indulgence and distraction. Generative AI, cloaked in helpfulness, embodies both. It doesn’t censor. It seduces. It doesn’t need Newspeak to delete ideas. It replaces them with prediction. In 1984, language was weaponized by force. In our world, it’s being reshaped by suggestion. What we have is not Artificial Intelligence—it’s Artificial Inference: trained not to understand but to remix, not to reason but to simulate. And this simulation brings us to a more profound loss: intersubjectivity. AI and the Loss of Intersubjectivity Humans learn, grow, and build reality through intersubjectivity—the shared context that gives language its weight. It allows us to share meaning, to agree on what a word represents, and to build mutual understanding through shared experiences. Without it, words float. AI doesn’t participate in intersubjectivity. It doesn’t share meaning—it predicts output. And yet, when someone asks an AI a question, they often believe the answer reflects their framing. It doesn’t. It reflects the average of averages, the statistical ghost of comprehension. The illusion of understanding is precise, polite, and utterly hollow. This is how AI reconditions reality at scale—not by force, but by imitation. The result? A slow, silent attrition of originality. Nouns lose their edges. Ideas lose their anchors. Authorship bleeds into prompting. And truth becomes whatever the model says most often. AI and Accountability: A Case Study in Trust and Miscommunication In one recent public example, Air Canada deployed an AI-powered chatbot to handle customer service inquiries. When a customer asked about bereavement fare discounts, the chatbot confidently invented a policy that didn’t exist. The airline initially tried to avoid responsibility, but the court disagreed. In February 2024, a tribunal ruled that Air Canada was liable for the misinformation provided by its chatbot. This wasn’t just a technical glitch—it was a trust failure. The AI-generated text sounded plausible, helpful, and human, but it lacked grounding in policy, context, or shared understanding. In effect, the airline’s brand spoke out of both sides of its mouth and cost them. This is the risk when language is generated without intersubjectivity, oversight, or friction. The Linguistic Drift of AI: What the Data Tells Us About Language Decay It’s not just theory—research is now quantifying how generative AI systems are shifting the landscape of language itself. A study titled Playing with Words: Comparing the Vocabulary and Lexical Richness of ChatGPT and Humans found that AI-generated outputs consistently use a narrower vocabulary, with significantly fewer nouns and stylistic words than human writing. Building on this, an analysis of over 14 million PubMed abstracts revealed measurable shifts in word frequency following the rise of LLM use. While many precise, technical nouns faded, terms like “delves” and “showcasing” surged. The shift is not random; it’s a statistically driven flattening of language, where standard, action-oriented, or stylistic terms are promoted, and specificity is sidelined. Some researchers link this to a broader problem known as “model collapse.” As AI models are increasingly trained on synthetic data, including their outputs, they may degrade over time. This leads to a feedback loop where less diverse, less semantically rich language becomes the norm. The result is a measurable reduction in lexical, syntactic, and semantic diversity—the very fabric of meaning and precision. The implications are vast. If AI systems are deprioritizing nouns at scale, then the structures we use to hold ideas—people, places, identities, and concepts—are being eroded. In real time, we are watching the grammatical infrastructure of human thought being reweighted by machines that do not think. What AI’s Language Shift Means for Brands and Business Strategy The erosion of language precision has significant implications for businesses, particularly those that rely on storytelling, branding, and effective communication. Brands are built on narrative consistency, anchored by nouns, identities, and associations that accumulate cultural weight over time. However, as AI systems normalize probabilistic language and predictive phrasing, even brand voice becomes a casualty of convergence. Differentiation erodes—messaging blurs. Trust becomes more complicated to earn and more uncomplicated to mimic. As this Forbes piece outlines, there are serious reasons why brands must be cautious with generative AI when it comes to preserving authenticity and voice. Moreover, AI-powered content platforms optimize for engagement, not meaning. Businesses relying on LLMs to generate customer-facing content risk flattening their uniqueness in favor of what’s statistically safe. Without human oversight, brand language may drift toward the generic, the probable, and the forgettable. How To Safeguard Meaning in the Age of AI Resist the flattening. Businesses and individuals alike must reclaim intentionality in language. Here’s how—and why it matters: If you don’t define your brand voice, AI will average it. If you don’t protect the language of your contracts, AI will remix it. If you don’t curate your culture, AI will feed it back to you—statistically safe but spiritually hollow. Double down on human authorship: Don’t outsource your voice to a model. Use AI for augmentation, not substitution. Protect linguistic originality: Encourage specificity, metaphor, and vocabulary diversity in your communication. Nouns matter. Audit your outputs: Periodically review AI-generated materials. Look for signs of drift—has your language lost its edge? Invest in language guardianship: Treat your brand’s lexicon like intellectual property (IP). Define it. Defend it. Champion intersubjectivity: Allow shared context in both personal and professional communication. AI can simulate, but only humans can mean. The Necessity of Friction: Why Human Involvement Must Temper AI Friction isn’t a flaw in human systems—it’s a feature. It’s where meaning is made, thought is tested, and creativity wrestles with uncertainty. Automation is a powerful economic accelerant, but without deliberate pauses—without a human in the loop—we risk stripping away the qualities that make us human. Language is one of those qualities. Every hesitation, nuance, and word choice reflects cognition, culture, and care. Remove the friction, and you remove the humanity. AI can offer speed, fluency, and pattern-matching, but it can’t provide presence, and presence is where meaning lives. AI’s Closing Refrain: A Call to Remember Meaning Emily M. Bender, a professor of computational linguistics at the University of Washington, has emerged as one of the most principled and prescient critics of large language models. In her now-famous co-authored paper, "On the Dangers of Stochastic Parrots," she argues that these systems don’t understand language—they merely remix it. They are, in her words, “stochastic parrots”: machines that generate plausible-sounding language without comprehension or intent. Yet we’re letting those parrots draft our emails, write our ads, and even shape our laws. We’re allowing models trained on approximations to become arbiters of communication, culture, and identity. This is not language—it’s mimicry at scale. And mimicry, unchecked, becomes a distortion. When AI outputs are mistaken for understanding, the baseline of meaning erodes. The problem isn’t just that AI might be wrong. It’s that it sounds so right, we stop questioning it. In the name of optimization, we risk erasing the texture of human communication. Our metaphors, our double meanings, our moments of productive ambiguity—these are what make language alive. Remove that, and a stream of consensus-safe, risk-averse echo remains. Functional? Yes. Meaningful? Not really. The stakes aren’t just literary—they’re existential. If language is the connective tissue between thought and reality, and if that tissue is replaced with statistical scaffolding, thinking becomes outsourced. Once sharpened by friction, our voices become blurred in a sea of plausible phrasings. Without intersubjectivity, friction, or nouns, we are scripting ourselves out of the story, one autocomplete at a time We are not being silenced. We are being auto-completed. And the most dangerous part? We asked for it. Before we ask what AI can say next, we should ask: What has already gone unsaid? In this quiet war, we don’t lose language all at once. We lose it word by word—until we forget we ever had something to say. I asked brand strategist and storyteller Michelle Garside, whose work spans billion-dollar brands and purpose-driven founders, to share her perspective on what’s at risk as automation flattened language. Her response was both precise and profound: If language is being flattened, we need more people doing the opposite: excavating. Listening for what’s buried beneath the noise. Uncovering the phrase that unlocks the person. That’s not a prompt—it’s a process. And it’s a deeply human one. When someone says something that lands—not because it sounds good, but because it’s true. You can see it in their body. You can feel it in the silence that follows. No algorithm can replicate that because that moment isn’t statistical. It’s sacred. The risk isn’t just that AI will get things wrong. It’s that it will sound just right enough to stop us from looking deeper. To stop us from asking what’s real. To stop us from finding the words only we could say. We don’t need more words. We need more meaning. And meaning isn’t generated. It’s remembered. When it comes to language and AI, that’s the line to carry forward—not just because it sounds good, but because it’s true.