A new art exhibition in Chicago uses more than 300 works of art to trace the historical origins of the word “homosexual,” mapping how it’s shaped our modern perception of queer identity. According to its lead curator, museums around the world have refused to show the exhibition due to the current political climate—even when it’s offered to them for free. The exhibition, titled The First Homosexuals: The Birth of a New Identity, 1869-1939, is currently on view at the Wrightwood 659 museum in Chicago through July 26. It’s the first time that the exhibition—a passion project of over eight years for lead curator Jonathan D. Katz—has been shown in its entirety. Installation view of The First Homosexuals: The Birth of a New Identity, 1869-1939, at Wrightwood 659, 2025. [Photo:Daniel Eggert/@DesigningDan] Through sculptures, paintings, prints, and other media from the late 19th and early 20th centuries, it explores early, oft-overlooked expressions of queer culture. Further, it examines how the coining of the term “homosexual” created a binary understanding of sexuality that we’re still grappling with today. The First Homosexuals sold more advance tickets than any other show since the Wrightwood 659 opened in 2018. But Katz says that after pitching the exhibition to many other museums, he’s been faced with one rejection after another.  Marie Laurencin, Le bal élégant or La danse à la campagne (The Elegant Ball, or The Country Dance), 1913, Oil on canvas, 112 x 144 cm, Musée Marie Laurencin, Tokyo. [Image: courtesy Wrightwood 659] A career in queer studies  Katz, who is a professor of queer art history at the University of Pennsylvania, began his career in queer studies during the Reagan administration. “When I started, my field was just being born,” Katz wrote in a biography for Northwestern University, where he received his PhD. “Reagan was in office, AIDS was being instrumentalized by the Right to justify the most odious forms of discrimination, and I had been kicked out of the University of Chicago (among other universities) for pursuing the relationship between art and sexuality.” In the decades since, Katz has gone on to teach queer studies at several different universities, including Yale, and cocurated a queer exhibition called Hide/Seek Difference and Desire in American Portraiture at Smithsonian’s National Portrait Gallery. Katz’s new exhibition is inspired by a question that’s followed him throughout his years of research.  “The minute you go outside of Europe and its colonies, questions of sexual difference assume a completely different meaning—which is to say that, very often, there’s absolutely no issue associated with same-sex sexuality, and it’s often understood as part of a continuum of sexualities,” Katz says. “I was interested, therefore, in trying to decenter the assumptions that we have about sexuality by reference to other cultural norms. That’s what motivated this exhibition, as well as a careful investigation of what, literally, the earliest representations look like.” The first use of the word “homosexual” Katz’s curiosity led him back to what’s believed to be the first-ever use of the word “homosexual,” found in a letter exchange between two queer activists, Karl Heinrich Ulrichs and Karl-Maria Kertbeny, in 1868. 1868 Letter. National Széchényi Library, Manuscript Collection. [Image: courtesy Wrightwood 659] In the letters, Kertbeny takes issue with Ulrichs’s relegation of queer individuals to its own class of people (or a “third sex.”). Instead, Kertbeny argued, everyone has the capacity for both “homosexual” and “heterosexual” desire. “What’s striking is that we use Kertbeny’s language [today], but we have unfortunately held fast to Ulrichs’ deeply minoritizing identity category,” Katz says. Andreas Andersen, Interior with Hendrik Andersen and John Briggs Potter in Florence,, 1894, Oil on canvas, 128.5 x 160 cm. Under licence from MiC – Direzione Musei Statali della Città di Roma – Photographic Archive; by kind permission of the National Museums Directorate of the City of Rome – Hendrik Christian Andersen Museum. [Image: courtesy Wrightwood 659] Both before and after Kertbeny and Ulrichs’s debate, queer sexuality existed on a spectrum—and it was captured by countless artists. The First Homosexuals includes works by 125 of them, from well-known artists like Jean Cocteau and the Lumière Brothers to lesser-known creatives like Jacques-Émile Blanche. They were pulled from an extensive list of sources, including both private collectors and institutions like MOMA.  Works include an 1820s depiction of men dressed as women on the streets of Lima, Peru; a series of scrolls from Japan in 1850 exploring the sexual education of a young man, who’s shown sleeping with both men and women in a variety of positions; and an 1891 photograph showing four women in a romantic embrace. The exhibition is divided into eight sections, each dedicated to peeling back a layer of a story that’s largely gone untold in the mainstream.  Alice Austen, The Darned Club, 1891, Original glass plate negative, 4 x 5 in, Collection of Historic Richmond Town. [Image: courtesy Wrightwood 659] The final portion of the exhibition is an archway wallpapered with photos of Nazis burning books at the Institute for Sexual Research, the world’s first queer rights organization. It’s a dark closing note that reminds viewers of the many archives of queer history that have been purposefully and violently hidden. “The idea that everything that flowers over the course of the exhibition can so quickly be destroyed, is, of course, a metaphor for where we are now,” Katz says. Since the exhibition opened on May 2, audience reactions have been striking. “It’s been profound,” Katz says. “Lots of emotion, tears, real delight, and a sense of a robbed history that’s being restored.” Elisàr von Kupffer, La danza, 1918, Oil on canvas with painted frame, 197 x 99 cm (framed). [Image: © Municipality of Minusio/Centro Elisarion, Claudio Berger (photo)/courtesy Wrightwood 659] A “terrible sign” for museums For now, though, that history might only be available to a select few.  When Katz first began outreach for collecting the art to be included in The First Homosexuals six years ago, he says 80 to 90% of his requests to museums and collectors were rejected—the highest rate of rejection he’s ever encountered. “There were a number of pieces that didn’t come because when you mount an exhibition about the first homosexuals, you know right going in that there are going to be places that just will not want to play with you,” Katz says. “And that was indeed the case.” Ida Matton, La Confidence (The Secret), 1902, Plaster, 65 x 56 cm, Photo: Joel Bergroth/Hälsinglands Museum. [Image: courtesy Wrightwood 659] Since then, rejections have continued to plague the exhibition. Katz has been pitching the finished show to museums around the world for nearly four years, in some cases even offering the exhibition for free despite its multimillion-dollar valuation, he told the Chicago Sun-Times. So far, he’s received near-universal rejections, with the exception of the Kunstmuseum Basel in Switzerland, which is currently in talks with Katz to display part of the exhibition at Art Basel 2026. Time and time again, Katz has received the same standard rejection notices from over 100 museums, including the Tate Britain. (The Tate did not respond to a request for comment by publication) Saturnino Herrán, Nuestros dioses antiguos, 1916, Oil on canvas, 101 x 112 cm, Colección Andrés Blaisten, México. [Image: courtesy Wrightwood 659] “I wish I knew more—I just get the rejection letters,” Katz says. “What I hear is, generally, ‘It doesn’t fit our programming,’ or ‘We’re fully scheduled,’ or some typical excuse.” But one director of a major museum, whose name Katz declined to share, did choose to elaborate further. “They said to me, ‘It’s exactly the kind of exhibition I want to show, and therefore it’s the exhibition I can’t show.” In several cases, Katz adds, the initial reception of the proposal was very promising, but it was ultimately turned down, leading him to wonder whether the museums’ boards were issuing the final “no.” In part, Katz attributes this reaction to a “hangover” from photographer Robert Mapplethorpe’s 1988 exhibition The Perfect Moment, which was cancelled by the Corcoran Gallery of Art in Washington, D.C., after conservative leaders heavily criticized the exhibition for containing homoerotic content. In the midst of the Reagan presidency, federal funding for the arts had become a hot-button issue, especially as it pertained to work that right-wing pundits labeled indecent.  It’s a period in history that feels like an uneasy echo of the arts scene today, as the Trump administration has moved to dismantle funding for local museums and libraries, canceled National Endowment for the Humanities grants, and blocked federal arts funding from going to artists who promote so-called “gender ideology,” a vague term that the government appears to be using as a dog whistle for any kind of gender expression outside of the binary. While Katz sent out most of his art loan requests and exhibition pitches before Trump’s election, he says this pattern of rejection is a familiar narrative that’s plagued the museum world for years. Tomioka Eisen, kuchi-e (frontispiece) with artist’s seal Shisen, c. 1906, Woodblock print, 23.2 x 31.6 cm, Tirey-van Lohuizen Collection. [Image: courtesy Wrightwood 659] “It may not be Trump’s horrific politics, but it is still horrific politics,” Katz says. “It’s the age old prejudicial politics that animates the museum world.” More generally, as a queer studies expert who faced repeated instances of institutional homophobia during the Reagan years, Katz feels that the current political attitude toward the queer community is “worse than a regression.” Tamara de Lempicka, Nu assis de profil, 1923, Oil on canvas, 81.2 x 54 cm, Döpfner Collection, Germany. [Image: Sotheby’s/courtesy Wrightwood 659] “Homophobia was actually bizarrely less naked under Reagan than it is under Trump,” Katz says. “They still hated us, but they talked about the idea of an inclusive culture. There’s no discourse of an inclusive culture now. There are clearly drawn borders and boundary lines in every sense of the word, and a profound sense of us against them.” For museums that are brave enough to speak out, Katz believes there could be an opportunity to build trust with new audiences by choosing to platform queer stories instead of silencing them. “I think that museums actually have a remarkable opportunity to build their audience and relevance if they seize it,” Katz says. “There is a large population that is not a veteran museum-going population that can become a veteran museum-going population by speaking to the social and political issues that haunt this country. That many museums try to avoid that desperately is a terrible sign. What museums need to do is frankly engage with it.” Source: https://www.fastcompany.com/91332097/this-exhibition-explores-the-history-of-the-term-homosexual-museums-are-afraid-to-show-it #this #exhibition #explores #the #history #term #homosexual #museums #are #afraid #show

Author(s): Talha Nazar Originally published on Towards AI. Imagine a world where your thoughts can control machines. You think, and a robotic arm moves. You feel, and a digital avatar mimics your expression. Sounds like science fiction, right? But this is no longer just an idea scribbled in a cyberpunk novel — it’s happening right now, at the intersection of neuroscience and artificial intelligence. As someone who’s been closely following AI for years, I find this confluence between biology and code deeply fascinating. It’s as if we’re uncovering a hidden mirror: AI reflects how we think, while neuroscience peels back the layers of what thinking even is. In this story, we’ll journey from brainwaves to neural networks, exploring how scientists and engineers are blending biology with silicon to create machines that learn, adapt, and maybe one day, even feel. The Brain as a Blueprint for Machines Let’s start with a simple question: How did AI get so smart? The answer lies partly in how closely it’s modeled after us. When researchers first began building artificial intelligence, they didn’t pull the idea from thin air. Instead, they looked inward — to the brain. Our brains contain roughly 86 billion neurons, each connected to thousands of others, forming a massive web of electrical and chemical signals. Early AI pioneers like Warren McCulloch and Walter Pitts were inspired by this structure. In 1943, they introduced a computational model of a neuron, laying the groundwork for what would later become artificial neural networks. Fast forward to today, and these neural networks form the backbone of AI systems like GPT, Siri, and autonomous cars. While far simpler than a real brain, they mimic how we process information: through layers of pattern recognition, memory, and adjustment based on feedback. “The brain is not a computer, but it teaches us how to build better ones.” The parallels are stunning. Just like we learn from experience, AI models use algorithms like backpropagation to tweak their internal weights — essentially fine-tuning their ‘memory’ to make better decisions over time. Weirdly, it’s like machines are learning to think the way we do. From Mirror Neurons to Machine Empathy Here’s where things get even more sci-fi. In 1992, neuroscientists in Italy discovered mirror neurons — special brain cells that activate both when we perform an action and when we observe someone else doing it. It’s like your brain says, “Hey, I know what that feels like.” These neurons are believed to be central to empathy, learning by imitation, and even language acquisition. Now, imagine giving machines a similar ability. That’s precisely what researchers are trying to do. AI systems like OpenAI’s CLIP or Google DeepMind’s Gato are trained across multiple modalities — text, images, audio, and more — to better understand human context and emotion. Of course, machines don’t feel. However, they can approximate emotional responses using vast datasets of human expression. Think of AI-generated art that captures loneliness, or chatbots that recognize your tone and respond with sympathy. Are they truly empathetic? Probably not. But can they simulate empathy well enough to be helpful? Increasingly, yes. And that opens up enormous potential — especially in fields like mental health, where AI tools could one day assist therapists by detecting early signs of distress in patients’ speech or facial expressions. Brain-Computer Interfaces (BCIs): Reading Minds, Literally Let’s go a step further. What if machines didn’t just respond to your words or actions — what if they could read your thoughts? That’s the promise of brain-computer interfaces (BCIs), a fast-growing area at the crossroads of neuroscience, AI, and hardware engineering. Companies like Neuralink (yes, Elon Musk’s venture) are developing implantable devices that allow the brain to communicate directly with computers. These chips record electrical signals from neurons and translate them into digital commands. That means someone paralyzed could one day send emails or move a robotic arm — just by thinking. Sounds incredible, right? But it’s not just Neuralink. UC San Francisco researchers recently used AI to decode brain activity into speech in real time. Meanwhile, non-invasive devices — like EEG headsets — are getting better at detecting focus, fatigue, and even emotional states. This isn’t just about convenience — it could redefine accessibility, communication, and even what it means to be human. Still, there are ethical challenges. Who owns your neural data? Can it be hacked? And what happens if the interface misfires? These questions aren’t just theoretical. As BCI tech scales, we’ll need policies to ensure it enhances autonomy rather than undermines it. Where They Merge: Shared Architectures and Inspirations As the convergence of AI and neuroscience deepens, we begin to see a fascinating blend of ideas and structures. AI models inspired by the brain are not just theoretical anymore; they are real-world tools pushing the boundaries of what we thought possible. Let’s break down some of the key areas where the two fields come together. 1. Neural Networks & Deep Learning When you look at deep learning models, you might notice something oddly familiar: the way they’re structured. Although artificial neurons are simpler, they resemble biological neurons in some ways. Deep learning models are designed with layers — just like the visual cortex in the human brain. Early layers of neural networks detect basic features like edges, and as the network gets deeper, it begins to recognize complex patterns and objects. This mimics the brain’s hierarchical system of processing information, starting from simple features and building up to complex recognition. It’s this analogy that has led to breakthroughs like image recognition and language translation. Illustration by Author — Napkin.ai 2. Reinforcement Learning and Dopamine Reinforcement learning (RL) is a type of machine learning where agents learn by interacting with an environment, making decisions, and receiving rewards. This idea of learning through rewards and punishments draws directly from neuroscience. In the brain, dopaminergic neurons play a huge role in reward-based learning. The basal ganglia, a part of the brain involved in motor control and decision-making, is activated when we receive a reward. Similarly, in reinforcement learning, an agent’s actions are reinforced based on a reward signal, guiding the system toward better choices over time. Illustration by Author — Napkin.ai 3. Memory and Attention Mechanisms Have you ever wondered how we remember important details in a conversation or a lecture, despite distractions around us? That’s the power of attention mechanisms in the brain. These mechanisms allow us to focus on the most relevant pieces of information and filter out the noise. In AI, this is mimicked by models like Transformers, which have taken the machine-learning world by storm, particularly in natural language processing (NLP). By paying attention to key parts of input data, Transformers can process entire sentences, paragraphs, or even entire documents to extract meaning more effectively. It’s what powers tools like ChatGPT, Gemmni, Grok, Deepseek, and many others. Illustration by Author — Napkin.ai 4. Neuromorphic Computing The field of neuromorphic computing is a fascinating intersection where hardware and brain science collide. Neuromorphic chips are designed to replicate the brain’s efficiency and power in processing. These chips aren’t just inspired by the brain’s architecture but also mimic the way the brain communicates via spiking neural networks, which process information in discrete pulses — similar to how neurons fire in the brain. Companies like IBM with TrueNorth and Intel with Loihi are leading the way in neuromorphic chips, creating highly energy-efficient processors that can learn from their environments, much like a biological brain. Illustration by Author — Napkin.ai Top Impactful Applications of the AI-Neuroscience Merge The possibilities that arise from the blending of AI and neuroscience are not just theoretical. They’re already shaping the future, from the way we interface with machines to how we treat mental health. Let’s explore some of the most groundbreaking applications. 1. Brain-Computer Interfaces (BCIs) If you’ve ever dreamed of controlling a machine with just your thoughts, then you’re in luck. Brain-computer interfaces (BCIs) are making this possible. Companies like Neuralink are developing technologies that allow individuals to control devices using only their brain signals. For example, BCIs could allow someone paralyzed from the neck down to move a robotic arm or type with their mind. The big breakthrough came in 2023 when Neuralink received FDA approval for human trials. While this is a huge step forward, it’s only the beginning. These technologies could revolutionize the way we interact with technology and provide life-changing solutions for people with disabilities. 2. Mental Health Diagnosis and Treatment We all know how complex mental health is. But AI has started to play a pivotal role in helping us understand and treat mental illnesses. Imagine an AI system that analyzes speech, text, and behavior to detect early signs of depression, anxiety, or even schizophrenia. Neuroscience has validated these AI models by comparing them with brain imaging techniques like fMRI. Recent studies have shown that combining fMRI scans with deep learning can predict suicidal ideation in individuals at risk, a breakthrough that could save countless lives. 3. Brain-Inspired AI Models AI is increasingly drawing inspiration from how the brain works. For example, DeepMind’s AlphaFold revolutionized protein folding predictions, but its inspiration didn’t come solely from computers. By studying how the brain processes information, DeepMind developed models that learn and adapt in ways similar to human cognition. This approach has given birth to models like Gato, a single neural architecture capable of handling hundreds of tasks — just as the human brain can handle a wide array of functions with efficiency and ease. 4. Neuroprosthetics One of the most inspiring applications of AI in neuroscience is in neuroprosthetics. These prosthetics enable people to control artificial limbs directly with their brain signals, bypassing the need for physical motion. The DEKA Arm is an example of a prosthetic that allows people with paralysis to control their arms through neural input, helping them regain lost independence. 5. Cognitive Simulation & Brain Mapping Understanding the human brain in its entirety — from the smallest neuron to the largest cognitive functions — is one of the greatest challenges of modern science. Projects like the Human Brain Project and Blue Brain Project aim to simulate entire regions of the brain using AI models trained on massive datasets. These initiatives could unlock the mysteries of consciousness and cognition, making the human brain one of the most powerful tools in science. The Future: Beyond the Intersection of AI and Neuroscience The future of AI and neuroscience is incredibly exciting, and we’re only just scratching the surface. As AI models become more advanced and neuroscience continues to uncover the brain’s mysteries, we’ll see more refined and powerful applications that can change our lives in unimaginable ways. 1. Personalized Healthcare Imagine a world where AI doesn’t just treat illnesses based on generalized data but tailors treatments to your unique brain structure. With advances in neuroimaging and AI algorithms, personalized medicine could become a reality. AI could analyze your brain’s unique structure and function to predict diseases like Alzheimer’s, Parkinson’s, or even mental health disorders, offering treatments designed specifically for you. 2. AI-Augmented Cognition In the distant future, we may see a world where AI enhances human cognition. Augmenting our natural intelligence with AI-driven enhancements could help us solve complex problems faster and more accurately. Whether it’s through direct brain interfaces or enhanced learning techniques, this fusion of AI and neuroscience could reshape human potential in ways we can’t even begin to fathom. 3. Artificial Consciousness At the intersection of AI and neuroscience, some are exploring the possibility of artificial consciousness — the idea that AI could one day become self-aware. Though this concept is still very much in the realm of science fiction, the continued merging of AI and neuroscience might eventually lead to machines that can think, feel, and understand the world just as we do. The ethical implications of such a development would be profound, but the pursuit of consciousness in AI is something many researchers are already investigating. Conclusion The merging of AI and neuroscience is not just a passing trend; it’s an ongoing revolution that promises to change the way we interact with machines, understand the brain, and even treat neurological conditions. While AI has already made incredible strides, the integration of neuroscientific insights will accelerate these advancements, bringing us closer to a future where human and machine intelligence work together seamlessly. With the potential to reshape everything from healthcare to personal cognition, the collaboration between AI and neuroscience is poised to transform both fields. The journey ahead is long, but the possibilities are endless. The brain — our most sophisticated and enigmatic organ — may soon be the blueprint for a new era of intelligence, both human and artificial. References Thank you for reading! If you enjoyed this story, please consider giving it a clap, leaving a comment to share your thoughts, and passing it along to friends or colleagues who might benefit. Your support and feedback help me create more valuable content for everyone. Join thousands of data leaders on the AI newsletter. Join over 80,000 subscribers and keep up to date with the latest developments in AI. From research to projects and ideas. If you are building an AI startup, an AI-related product, or a service, we invite you to consider becoming a sponsor. Published via Towards AI Source: https://towardsai.net/p/artificial-intelligence/merging-minds-how-neuroscience-and-ai-are-creating-the-future-of-intelligence #merging #minds #how #neuroscience #and #are #creating #the #future #intelligence

There’s a routine, but profoundly telling moment when many visitors complete their first walkthrough at Future Pastime, the exhibition of paintings by the visionary artist and visual futurist Syd Mead currently on display in Manhattan’s Chelsea neighborhood. “His outlook on the future is so positive. I thought Syd Mead was dystopian,” many attendees exclaim, as if on cue.  Space Wheel Interior (1979) This perception is heavily guided by the work that—-for many—is the single most widely known reference point for Mead’s impact as a visual futurist: Director Ridley Scott’s 1982 rain soaked, techno-dystopian masterpiece, Blade Runner, whose visual landscape was largely Mead’s creation. In Blade Runner, Mead’s dystopian Los Angeles is so sleek, so complex, so rich with detail, one cannot blame audience members for confusing him with being a master of the morose. However, to quote Blade Runner 2049 director Denis Villeneuve (and another Mead collaborator): “[Syd] traveled in dystopia only once, and it was because of Ridley Scott. Syd’s first drawings of Los Angeles for Blade Runner were pure, bright and peaceful, but Ridley wanted his new world to be more claustrophobic and oppressive. And Syd dived into the darkness.”  One would reasonably expect Mead, with his fluent visual language of darkness so ably on display in Blade Runner, to be a committed future-cynic. However, it was quite the opposite. Mead was, in Vileneuve’s words, “one of the last great utopians,” a tidbit often lost on audiences raised on a steady diet of apocalyptic scenarios, dismal futures, and collapsed civilizations—the same narrative requests made often by Mead’s commissioning directors. In fact, Mead’s output outside the world of cinema—which in fact represents the lionshare of his more than 65-year professional career—is a replete and uniform world of robust optimism and hopeful aspiration. The future of Syd Mead is a bright and gilded one; the fulfillment of our greatest hopes and aspirations. It exists on the distant end of realism, yet is still somehow within reach.  The son of a Baptist minister (and part time art teacher), Mead gathered the strands of a childhood crowded by poverty and increasing world strife and instead formulated a unique worldview and artistic direction that was irrepressibly optimistic, often in spite of (and in stark contrast to) the current affairs and fortunes of the time.  To Mead, the prospect of an optimistic future was not a question of chance but of preparation. “Why wouldn’t you rehearse for a good future?” he often said. “I think that we should celebrate and rehearse for a bright future, and maybe it will come true. I don’t have time to illustrate misery or dystopian scenarios because they’ll happen. If you let everything go, they’ll happen anyway.” This outlook was successfully channeled into a singular career as an industrial futurist, becoming one of a rare group of individuals kept on speed dial by titans of industry to predict and illuminate likely future outcomes across myriad disciplines: architecture, urban planning, engineering, automotive, aeronautics, mass transit, spaceflight, technology, innovation, consumer goods, media, and more. Mead took it a step further by assuming the mantle of “visual futurist,” delivering his results not in research papers or dissertations, but via vivid, dynamic artworks, most principally paintings in his medium of choice: gouache.  Running of the 200th Kentucky Derby (1975) What he never anticipated was that the language of his industrial forecasting work would escape its enclosure and go on to define the visual identity of modern science fiction storytelling and cinematic futurism.   Mead entered professional life with nary a thought nor desire to work in the movies. In fact, movies were banned in the Mead household until he was 13 years old, with his earlier years filled with a potent mixture of his father’s chief obsessions: end-times religion, study and practice of painting and fine arts, and the pulp science fiction adventures of Buck Rogers and Flash Gordon (which Kenneth, his father, would buy family friendly editions so they could read together). Syd’s personal contributions to this percolating artistic concoction—an obsession with automobiles, the vibrant creations of contemporary illustrators as diverse as Maxfield Parrish and Chesley Bonestell, and the constant desire to innovate and improve one’s artistic craft—provided the ingredients to a simmering brew that one day would synthesize into the poetic future seer we celebrate today. By his mid-teens, Mead distanced himself from the old time religion of his parents as a new devotion emerged:bright future. The works of his adolescence and early adulthood burst with an almost unbridled gusto of color and life as potential futures were considered. Cowboys draped in pastel tour rural ranch grounds on horseback and bi-copter. Flying transport, early prototypes of Blade Runner-esque vehicles to come, twenty five years before the fact, are wrapped in chrome and almost seem to lift off the page. Join our mailing list Get the best of Den of Geek delivered right to your inbox! After his 1959 graduation from the Art Center in Los Angeles where he majored in industrial design, word quickly spread of this young man with a “future touch:” the uncanny ability to conceptualize and render achievable futures with the dexterity of an industrial engineer, the functionality of a city planner, and the poetry of a master painter. His first post-college berth at Ford’s Advanced Car Styling studio—where he worked on futuristic concept cars like the Gyron—lasted all of 26 months. The “thinness of purpose” got to him, he said. “If the company decided to stop making cars and start making washing machines next week, the process would not change at all—you just start doing sketches of washing machines.”  Mead departed Ford in 1961, and it was a providential overture made by industrial giant U.S. Steel shortly prior that which truly set the stage for the budding visual futurist to prove his worth. Aluminum, both lighter and cheaper, had been steadily eating into U.S. Steel’s profits, as well as the public’s romance with the once iconic alloy. The task for Mead was clear: to portray steel as a material of the future. Fifty years. One hundred years. Beyond. To make it relevant again today by showcasing its dreamy successes of tomorrow.  It was the assignment he had been practicing for his whole life, and Mead did not disappoint. Essentially given carte blanche, he remarkably completed the entire book—text and images—in just 30 days. While first intended as a product marketing catalogue aimed squarely at U.S. Steel customers, clients, and manufacturing partners, his work was such a sensation that the company commissioned four more books through 1969, and expanded the distribution of the volumes, running adverts for free copies in young people’s magazines, and seeding editions to all of the major art and design schools across the country. The result was the 1960s equivalent of a viral sensation; the books “went horizontal through the design community,” as Syd put it himself. Blossoming directors, designers, technologists, and innovators sought out the books and cherished them as cult objects. Sacred windows into the future of both technology and design. Acclaimed genre director Joe Johnston, who cut his teeth as art director on the original Star Wars Wheeless Truck (1969) As a result, the “Syd Mead look” is all over Star Wars, very specifically in the case of the AT-ATs, the iconic tall legged imperial transports featured in The Empire Strikes Back, which were derived by Johnston and the Lucasfilm team directly from Mead’s “Wheelless Truck” Despite his overwhelming influence on the series, Mead was not involved directly in Star Wars. He wasn’t even asked. It’s an understandable oversight. The concept of calling up Syd Mead in 1975 (by then an acclaimed industrial designer and futurist) to work on a motion picture is equivalent to asking Zaha Hadid in her prime to design buildings for an episode of Dynasty. Requests like these were simply not made, and as the 20th anniversary of Syd Mead’s professional career approached, he had yet to work on a single motion picture, let alone even be asked. This all changed when a fortuitous thought entered the mind of Star Wars VFX director John Dykstra in early 1979. Dykstra, who had attended Long Beach State alongside Johnston, and maintained a personal collection of Mead’s U.S. Steel books, now found himself as the visual effects lead on Star Trek: The Motion Picture. With the design of the film’s central antagonist—a leviathan entity of “unimaginable scale” named V’ger—suddenly in jeopardy, Dykstra took a leap of faith and reached out to Mead, uttering 12 words that would change both film and futurism forever:  “Syd, would you like to work on a science fiction motion picture?” Syd replied, “Sure.” In this one moment, the circuit was completed, and the board lit up. Suddenly alerted to the fact that their favorite futurist was down to do film, the entire town pivoted to Mead in unison. Ridley Scott, Steven Lisberger, Peter Hyams, James Cameron, and John Badham all reached out in quick succession. By 1986, via Blade Runner, Tron, 2010, Aliens, and Short Circuit, the very aesthetic of science fiction was altered forever… not by the hand of an imaginative production designer, but an actual futurist trained on developing real world futures to be built.     Elon Solo is the co-curator of Syd Mead: Future Pastime, an exhibition exploring the original artwork of Syd Mead, currently on display in NYC through May 21.  City on Wheels (1969) Source: https://www.denofgeek.com/movies/syd-mead-is-not-science-fiction/ #syd #mead #not #science #fiction

Equipping LLMs with external tools or functions has become popular, showing great performance across diverse domains. Existing research depends on synthesizing large volumes of tool-use trajectories through advanced language models and SFT to enhance LLMs’ tool-calling capability. The critical limitation lies in the synthetic datasets’ inability to capture explicit reasoning steps, resulting in superficial tool call training. In many cases, reasoning is either completely omitted during the training or deferred to inference through prompting techniques. This results in pseudo-reasoning: models merely learn to mimic surface-level patterns without truly understanding the underlying decision-making process. Existing research explores multiple approaches to enhance LLMs’ tool-use capabilities. Previous methods have focused on two key strategies for improving tool learning. The first approach concentrated on dataset curation and model refinement, involving the creation of large-scale supervised datasets and applying advanced training techniques such as SFT and DPO reinforcement learning. LLMs are combined with various external tools, including search engines, calculators, vision tools, and Python interpreters, to expand their functional capabilities. The second approach targeted reasoning improvement, shifting from traditional train-time scaling to more complex test-time scaling strategies. Earlier methods relied on step-level supervision and learned reward models to guide reasoning trajectories. Researchers from NVIDIA, Pennsylvania State University, and the University of Washington have proposed the Nemotron-Research-Tool-N1 series to address the limitations of existing tool-use methods. It diverges from traditional SFT and reasoning trace distillation techniques by implementing a unique RL paradigm. Drawing inspiration from DeepSeek-R1’s success, a lightweight supervision method has been developed to focus on the structural validity and functional correctness evaluation of tool invocations. The Nemotron-Research-Tool-N1 model employs a binary reward mechanism that enables the model to autonomously develop reasoning strategies without relying on explicitly annotated reasoning trajectories. Researchers unify and preprocess data from existing tool-calling datasets, xLAM, and a subset of ToolACE, which provide single-turn and multi-turn synthetic tool-calling trajectories. A lightweight prompting template is created to guide tool call generation, featuring explicit instructions for intermediate reasoning within <think>…</think> tags and tool invocation enclosed in <tool_call>…</tool_call>. The template helps to minimize rigid formatting constraints and reduce the risk of overfitting to specific prompt patterns. The primary backbone model utilized is Qwen2.5-7B/14B-Instruct, and to evaluate the generalization ability of the proposed method, evaluations are performed on alternative backbone models, including multiple variants from the LLaMA family. Results on the BFCL and API-Bank benchmarks show Nemotron-Research-Tool-N1 models’ superior performance. On the BFCL benchmark, the Tool-N1-7B/14B models outperform closed-source models like GPT-4o and specialized fine-tuned models such as xLAM-2-70B and ToolACE-8B. The models surpass SFT baselines trained on identical data sources, highlighting the effectiveness of the R1-style RL approach. Further, the API-Bank benchmark validates these findings, with Tool-N1-7B/14B achieving 4.12% and 5.03% higher accuracy than GPT-4o. These results conclusively demonstrate the potential of the proposed method in enhancing large language models’ tool-calling capabilities through a novel reinforcement learning paradigm. In conclusion, researchers introduced Nemotron-Research-Tool-N1, a significant advancement in LLM tool-use capabilities. The research shows a paradigm shift from traditional SFT methodologies by introducing a novel rule-based RL approach. The proposed method enables models to develop sophisticated reasoning strategies without relying on explicitly annotated reasoning trajectories. Benchmark evaluations across BFCL and API-Bank consistently validate the approach’s effectiveness, showing substantial performance improvements over existing baselines. The findings open new avenues for developing more adaptable and intelligent language models that can autonomously generate reasoning strategies. Check out the Paper 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 90k+ ML SubReddit. Here’s a brief overview of what we’re building at Marktechpost: ML News Community – r/machinelearningnews (92k+ members) Newsletter– airesearchinsights.com/(30k+ subscribers) miniCON AI Events – minicon.marktechpost.com AI Reports & Magazines – magazine.marktechpost.com AI Dev & Research News – marktechpost.com (1M+ monthly readers) Partner with us 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/RL^V: Unifying Reasoning and Verification in Language Models through Value-Free Reinforcement LearningSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/Offline Video-LLMs Can Now Understand Real-Time Streams: Apple Researchers Introduce StreamBridge to Enable Multi-Turn and Proactive Video UnderstandingSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/AI That Teaches Itself: Tsinghua University’s ‘Absolute Zero’ Trains LLMs With Zero External DataSajjad Ansarihttps://www.marktechpost.com/author/sajjadansari/This AI Paper Introduce WebThinker: A Deep Research Agent that Empowers Large Reasoning Models (LRMs) for Autonomous Search and Report Generation Source: https://www.marktechpost.com/2025/05/13/reinforcement-learning-not-fine-tuning-nemotron-tool-n1-trains-llms-to-use-tools-with-minimal-supervision-and-maximum-generalization/ #reinforcement #learning #not #finetuning #nemotrontooln1 #trains #llms #use #tools #with #minimal #supervision #and #maximum #generalization

Humans display different ways of attaching to one another, shaped largely by how our caregivers responded to us in early childhood. Some of us may shy away from closeness and prefer emotional distance, while others feel anxious in relationships, seeking reassurance and fearing abandonment. Understanding these underlying attachment styles can help us navigate emotions and relationships — and, as it turns out, might even help us better understand ourselves through the eyes of our primate relatives.A recent study led by the Ape Social Mind Lab at France's University of Lyon offers insight into mother-offspring attachment types in wild chimpanzees. Their findings suggest that some patterns we assumed were uniquely human may be more deeply rooted in our evolutionary history — while others may be distinctly modern.The Different Attachment StylesAttachment theory, grounded in the work of 20th-century researchers John Bowlby and Mary Ainsworth (among others), proposes that children develop mental templates, or “attachment styles,” based on how consistently and sensitively their caregivers respond to their emotional needs. The styles include:Secure attachment arises when caregivers are reliably supportive. The child feels safe, explores confidently, and trusts others.Anxious attachment emerges when caregiving is inconsistent. The child may become clingy, fearing abandonment and needing constant reassurance.Avoidant attachment develops when caregivers are emotionally distant or rejecting. These children tend to suppress their emotional needs and rely only on themselves.Disorganized attachment, often linked to trauma or fear-inducing caregiving, combines both approach and avoidance behaviors — wanting comfort, but also fearing it.In psychological research, secure, anxious, and avoidant styles are considered “organized” because they reflect clear, if not always ideal, strategies for seeking support. Disorganized attachment, by contrast, is erratic and conflicted, and often associated with later emotional struggles.Wild Chimpanzees and HumansResearchers observed 50 wild chimpanzees in Côte d’Ivoire (or Ivory Coast, West Africa) over nearly 4,000 hours and found that their young showed only organized attachment styles — either secure or insecure-avoidant. Some infants stayed close to their mothers during threats, showing secure-like behavior. Others remained more independent, resembling avoidant styles. But perhaps what is most striking is what researchers didn't find.“In the wild, we found no evidence of disorganized attachment patterns, which supports the hypothesis that this type of attachment may not be an adaptive survival strategy in the face of environmental constraints,” said lead author Eléonore Rolland in a news release.This differs sharply from humans, where studies suggest around 23.5 percent of children display disorganized attachment. Among captive, peer-raised chimpanzees, that number jumps to 61 percent, a clue that caregiving environment plays a massive role in shaping attachment.“By identifying attachment patterns in wild chimpanzees, we provide important insights into the roots of human social behavior,” added senior author Roman Wittig in the statement.Rethinking Parenting Through a Primate LensSo, what does this mean for us?If disorganized attachment is nearly nonexistent among wild chimps — but relatively common in modern human societies — it raises important questions about how contemporary caregiving practices align with our evolutionary needs.“Our results deepen our understanding of chimpanzees’ social development and show that humans and chimpanzees are not so different after all,” said Rolland in the release. “But they also make us think: have some modern human institutions or caregiving practices moved away from what is best for infant development?”As senior author Catherine Crockford notes, “The high prevalence of disorganized attachment in humans and captive orphan chimpanzees, in contrast to wild chimpanzees, also supports the idea that the rearing environment plays an important role in shaping attachment types.”These findings not only deepen our understanding of our primate cousins but also invite us to reflect on how early relationships shape emotional health — for chimps and for us.Article SourcesOur writers at Discovermagazine.com use peer-reviewed studies and high-quality sources for our articles, and our editors review for scientific accuracy and editorial standards. Review the sources used below for this article:Journal of Pediatric Health Care: A REVIEW OF ATTACHMENT THEORY IN THE CONTEXT OF ADOLESCENT PARENTINGHaving worked as a biomedical research assistant in labs across three countries, Jenny excels at translating complex scientific concepts – ranging from medical breakthroughs and pharmacological discoveries to the latest in nutrition – into engaging, accessible content. Her interests extend to topics such as human evolution, psychology, and quirky animal stories. When she’s not immersed in a popular science book, you’ll find her catching waves or cruising around Vancouver Island on her longboard. Source: https://www.discovermagazine.com/planet-earth/what-chimpanzees-can-teach-us-about-attachment-styles-in-relationships #what #chimpanzees #can #teach #about #attachment #styles #relationships

I’m not the first to say it, but the generative AI boom is here – and it’s bringing massive opportunities for creative learning.In fact, we’re entering the age of the AI personal tutor. Of all the learning media available, personal, one-on-one tutoring has the strongest evidence for learning impact. According to educational psychologist Benjamin Bloom’s 2 sigma, this kind of tutoring is actually two standard deviations better than other learning models.Of course, generative AI isn’t a human tutor, and we have yet to see if an AI personal tutor will be able to replicate the learning motivation and efficacy that comes from human connection and empathy. But what is perhaps more promising is the potential for collaboration between AI and human tutors to increase both tutor productivity and learning impacts for students. In any case, it’s clear that we’re on the cusp of AI-boosted teaching and learning, and these advancements are coming much faster than human behavior can adapt.So, how do we change our mindset and behavior to maximize our potential with generative AI? And, what does that mean for creators?I had the opportunity to hear Sam Altman, founder and CEO of OpenAI, speak at the 2023 ASU + GSV Summit in April. Here’s how he said he would approach generative AI if he were just starting his career:“I would get as comfortable with the tech as I could. Really try to develop a native feel for it, prepare myself for a high rate of change in the world […] and a lot of resilience and ability to adapt to new things and try new things quickly.”I think Sam is right about the importance of resilience and adapting rapidly. As I discussed in my last blog post, games are one of the best learning media to teach us “the resilience to keep trying and learning from our failures, the creativity to face a new problem and solve it with no rulebook, and the resourcefulness needed to figure out solutions.” While it may seem like generative AI is poised to make resilience and resourcefulness unnecessary, I’d argue that learners will just need enhanced critical thinking skills to deftly evaluate solutions and make the right choices.I’m not only recommending we play more games that build our resilience and adaptability – I’m also recommending we start adapting our behavior with generative AI by gamifying how we learn to use it.As you do when beginning a new game, get curious and start tinkering with the hundreds of AI tools that have popped up over the last few months. Choose a few of your favorites, commit to embedding them in some key parts of your day to day, and take note of what you discover about your own learning. Learn something new that you never thought you could do – some of my favorite stories are of people who couldn’t build a video game in Unity at first, but are now doing it in a few hours. Play together with your team or community and talk about what’s helping you save time, create higher-quality experiences, and expand your creative possibilities. Measure and track your learning achievements with generative AI: Where are you more productive? How many new things have you learned? How are you changing how you learn?There’s incredible potential here to maximize human creativity. Chris Dede, professor in Learning Technologies at the Harvard Graduate School of Education, describes the ideal state:“As we understand the complementarity between human strengths and the strengths of AI, what is ideally going to happen in the workforce is a partnership between humans and AI called IA, Intelligence Augmentation. In this relationship, the person does what he or she does well, the AI does what it does well, and the whole is more than the sum of the parts. The partnership can accomplish more than either side can on its own.”At Unity, our hypothesis is that generative AI will empower creators to dream bigger than ever before. We believe we should be learning to create and leveraging generative AI at the same time.Hopefully, learning to create becomes more accessible to more people, and the possibilities for innovation continue to grow as we let AI handle the time-consuming tasks it’s best-suited for. Then, over time, we’ll be expanding our collective capacity to truly make the world a better place with more creators in it. Source: https://unity.com/archive/blog/news/ai-changing-how-we-learn-and-create #changing #how #learn #and #create #heres #why #you #should #lean