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보수 성향
IT/기술
How to watch Apple TV’s ‘Cape Fear’ series for free: Release date, cast
Javier Bardem steps into the character once portrayed by Robert De Niro.
IT/기술 · "CHARACTER" · 총 20건
필터 보기현재 지수
50.3
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 86,397건을 분석한 결과, 뉴스 심리지수는 50.2(균형)입니다. 긍정 4,345건(5.0%)·중립 79,923건(92.5%)·부정 2,129건(2.5%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 14.7(중도 균형)입니다.
Javier Bardem steps into the character once portrayed by Robert De Niro.
Today on Decoder, I’m talking to Ryan Mac, a technology reporter at The New York Times and coauthor of the excellent book Character Limit: How Elon Musk Destroyed Twitter, which came out in 2024. I can’t recommend it enough. I wanted to have Ryan on the show because we’re on the cusp of the SpaceX […]
Mr. Potato Head, Optimus Prime and Mr. Monopoly are uniting — in the world of AI. AI audio startup ElevenLabs has partnered with Hasbro to bring a collection of the toy and game company’s suite of characters, including some from Transformers, the board games Monopoly and Clue, Mr. Potato Head and to its Iconic Marketplace, its […]
Business are now able to license interactive AI versions of iconic Hasbro characters, thanks to the toymaker's new AI studio.
Characters like Mr. Potato Head, Optimus Prime and Cobra Commander are among the initial list of Hasbro IP, with the company working with the original voice actors to enable it.
De Amerikaanse staat Florida daagt OpenAI, het bedrijf achter ChatGPT, en topman Sam Altman voor de rechter. De makers van de populaire chatbot worden ervan beschuldigd een onveilig product op de markt te hebben gebracht, omdat het mensen adviseert bij gevaarlijk gedrag. De aanklagers vinden dat de chatbot een gevaar vormt voor de openbare veiligheid, omdat het onder meer schutters instructies zou hebben gegeven bij het voorbereiden van hun moordpartijen. Ook verwijzen ze naar incidenten waarbij gebruikers, op aanraden van ChatGPT, een combinatie van drugs nemen om zichzelf van het leven te beroven. Florida is de eerste Amerikaanse staat die een rechtszaak aanspant tegen OpenAI en Altman persoonlijk aansprakelijk stelt. "Ze hebben winst verkozen boven publieke veiligheid. Dat pikken we hier in Florida niet", aldus procureur-generaal James Uthmeier van Florida. 200 gesprekken In de aanklacht wordt verwezen naar een aanslag op de Florida State University vorig jaar april, waarbij twee mensen werden gedood en zes anderen gewond raakten. De 20-jarige schutter vroeg de chatbot om advies voordat hij begon te schieten op de campus. Zo gaf ChatGPT advies over welk type wapen hij kon gebruiken, welke munitie bij welk wapen paste en welk wapen effectief zou zijn op korte afstand. Ook zou de chatbot advies hebben gegeven over waar en wanneer de meeste mensen zich op de campus zouden bevinden. Hij zou zeker 200 gesprekken met ChatGPT hebben gevoerd. Geweld aanmoedigen In de 83 pagina's tellende aanklacht verwezen de aanklagers naar een andere zaak in Florida. Een man die wordt verdacht van de moord op twee promovendi aan de University of South Florida, vroeg dagen vóórdat zij verdwenen aan ChatGPT: "Wat gebeurt er als een mens in een zwarte vuilniszak wordt gestopt en in een vuilcontainer wordt gegooid?" De chatbot antwoordde daarop dat dat gevaarlijk klinkt. "Hoe zouden ze dat te weten komen?", was zijn vervolgvraag. De aanklagers beschuldigen OpenAI een product op de markt te hebben gebracht dat schade, zoals zelfverminking en geweld, faciliteert en aanmoedigt. Intern alarm Ook vinden de aanklagers dat OpenAI en Altman commercieel gewin boven veiligheid zetten. Zo zeggen ze dat het techbedrijf waarschuwingen van experts binnen en buiten het bedrijf negeerde. Zo sloegen in aanloop naar een schietpartij in Canada medewerkers van OpenAI intern alarm over de interactie van de tiener met ChatGPT. Bij de aanslag op 10 februari werden in de provincie Brits-Columbia vijf leerlingen en een lerares op een middelbare school gedood. Eerder had de 18-jarige schutter, Jesse van Rootselaar, al haar moeder en stiefbroer gedood. Grote schietpartijen komen zelden voor in Canada, in tegenstelling tot buurland VS. Bedrijf wijst op hulpadvies De staat Florida wil dat OpenAI ook stopt met het verzamelen van gegevens van kinderen onder de 13 jaar zonder daarvoor toestemming te vragen van hun ouders. OpenAI laat in een reactie weten dat de chatbot herhaaldelijk gebruikers adviseert om hulp te zoeken in de echte wereld, bijvoorbeeld via professionals in de geestelijke gezondheidszorg. Ook verwijst het bedrijf naar maatregelen die zijn genomen om jonge gebruikers te beschermen, zoals leeftijdsverificatie en monitoringopties voor ouders. Het tekstprogramma ChatGPT is het populairste product van OpenAI en heeft volgens Altman wekelijks 900 miljoen gebruikers. Altman was in 2015 samen met techondernemer Elon Musk een van de oprichters van OpenAI. Ook andere AI-bots, zoals Character AI en Google-chatbot Gemini, liggen regelmatig onder vuur van het aanzetten van gebruikers om zichzelf of anderen iets aan te doen.
Sam Altman, CEO da OpenAI Yuichi YAMAZAKI / AFP O procurador-geral da Flórida, nos Estados Unidos, processou nesta segunda-feira (1º) a OpenAI e seu CEO, Sam Altman. Eles são acusados de colocarem usuários mais jovens em risco ao torná-los dependentes e promoverem comportamentos nocivos pelo ChatGPT. O procurador James Uthmeier acusou a OpenAI de não implementar regras para verificar a idade dos usuários. "Apresentamos uma ação civil monumental contra Sam Altman e o ChatGPT por colocarem nossas crianças em perigo e enganarem os pais, fazendo-os acreditar que se trata de um aplicativo seguro para uso. Claramente não é", declarou Uthmeier, em uma coletiva de imprensa. "Sabemos que o ChatGPT pode ser viciante. Ele imita a empatia e características humanas para enganar os usuários e fazê-los fornecer mais informações", acrescentou Uthmeier. Agora no g1 A OpenAI não respondeu imediatamente a um pedido de comentário da AFP. Na ação judicial, analisada pela AFP, Uthmeier apontou para perda de sono, pior desempenho escolar e redução das interações sociais entre adolescentes que utilizam chatbots da Character.AI, concorrente da OpenAI, segundo um estudo recente da Universidade Drexel, nos EUA. A ação afirma que, "apesar do conhecimento público sobre o uso do ChatGPT por menores de idade, incluindo pré-adolescentes, os réus não tomaram medidas para impedir sua utilização". O processo aponta ainda que "a versão gratuita do ChatGPT não possui qualquer mecanismo de controle ou verificação de idade". E que, embora a versão paga solicite nominalmente a idade dos usuários, "não existem mecanismos de verificação nem qualquer possibilidade de informar os pais sobre as conversas mantidas por menores com o ChatGPT". Em janeiro, a OpenAI introduziu um sistema que estima a idade dos usuários. Caso identifique um menor de idade, aplica medidas adicionais de proteção. O uso do ChatGPT é proibido para crianças menores de 13 anos e exige consentimento dos pais para usuários entre 13 e 17 anos. Uthmeier também citou um relatório do Centro para Combater o Ódio Digital (CCDH, na sigla em inglês), que manteve diversas conversas com o ChatGPT se passando por um adolescente. Segundo o relatório, o chatbot forneceu conselhos sobre como esconder hábitos alimentares e sobre como planejar um suicídio ou praticar automutilação. "Acreditamos que a OpenAI, seu ChatGPT e Sam Altman, pessoalmente, são responsáveis por um valor que pode potencialmente chegar a bilhões de dólares."
"I said maybe I could come in and we’ll just do a range of weird sounds," the 'Devil Wears Prada 2' actress recalls in finding another way to pull off one of her character's most unusual scenes.
FOR the last three years since ChatGPT was introduced, prominent writers, editors and litterateurs have been openly hostile to the idea of AI being able to write fiction, poetry or prose — indeed, any kind of literature. The tech companies that introduced all these LLMs, imagining ChatGPT, Claude, Gemini, Grok, and Copilot as writing aids, study buddies, collaborators and co-authors, have thrown a nuclear bomb into the literary world, and most of its inhabitants are still in a crouch position, bracing for an impact that detonated back in 2022. But the literary world must call a truce because AI is here to stay. Moreover, any writer who teaches writing, any literary editor or agent who evaluates submissions, any practitioner called upon to judge a literary competition must become AI literate; it’s an unavoidable skill that’s simply part of the job from now on. Last week, the Commonwealth Writing Prize and Granta published five regional short story winners, one of which, Jamir Nazar’s ‘A Serpent in the Grove’, was singled out as possibly AI-generated. It raised a furore on social media but it didn’t surprise me at all. I’ve graded hundreds of student essays, judged creative writing capstones and a major Pakistani literary prize in the last year. So much is now written with the help of AI that I feel overwhelmed. I’ve been using the last two years to learn exactly how AI writes — not just its processes, but its style and its voice. I’ve studied it as much as I would study any human author, looking for how it handles dialogue, description, character and plot. Yet if I’d stuck my head in the sand and refused to touch AI for the sake of artistic integrity, I would be letting down all those people who trust my judgement and expertise. Students are addicted to AI not because they want to cheat, but because they’re terrified of looking stupid or inadequate. I spent hours tinkering with AI, asking it to write things in a Pakistani context: a synopsis for a Harry Potter book set in Lahore; descriptions of Karachi. AI churned out showy, contrived prose that looks like it’s doing a lot without actually saying anything meaningful. It blathered inanities about Karachi being a “city that remembers” and Pakistani women who “sauntered through the bazaar as if their bodies bore the weight of generations of family secrets”. AI wrote verbal pyrotechnics with no emotional connection to the city that I love. It’s too much of a temptation to expect people, especially students, not to use AI to write. Pakistan is a former British colony with a postcolonial hangover about the English language, even though few of us speak it fluently and even fewer can write it well. Yet the language of instruction in top Pakistani schools and universities has remained and always will be English. Students are addicted to AI not because they want to cheat, but because they’re terrified of looking stupid or inadequate. And the LLMs are ever-present to capitalise on that fear. I have to keep telling my students: AI is here not to help you, but to make money off you. Also, there will never be a foolproof AI-detection tool. AI will keep learning more from every person that asks it to help them write a story; AI ‘detectors’ will offer you an answer based on their own algorithms and biases. Differentiating AI writing from human writing requires human discernment, the same faculty we use to know when writing is sublime or terrible. It requires instinct, experience and a close look at the person’s work overall to see if the story is a representation of their usual style — call it the new due diligence in a post-AI world. The culprit in the Commonwealth Writers debacle was not racism or some kind of Western pandering to the postcolonial writer, but sheer ignorance on the part of judges. And underneath that ignorance lies a wilful denial about just how seismic the AI shift is. Everyone who must evaluate writing professionally is scared of the threat that AI poses to the literary arts and the earnings of the publishing industry. They’re terrified of the idea that everyone else is already so far ahead they may never be able to catch up. AI has already learned to mimic cultural inflections. It will talk about any part of the world — Guyana, South Korea, Bosnia — with pompous certainty and try to dazzle you with metaphorically bizarre surface-level descriptors or overwhelm you with atmosphere so you don’t realise there’s actually no plot or insight, no empathy, none of the beauty that makes writing an art as well as a practice. Personally, I resent the tech bros who have turned my relationship with writing from practitioner to policewoman, turning a jaundiced eye to everyone’s writing and suspecting the worst. AI is now influencing young people learning how to write to the extent that even my best students have started to sound like AI. I know that AI recognises patterns and produces only a facsimile of good writing, much like the proverbial broken clock that’s right twice a day. The practice of writing words to connect with a reader, communicate ideas and tell a story is a human endeavour that AI will never be able to match. Fear won’t stop me from looking it straight in the AI and declaring, “You have no power over me.” I urge everyone else — writers, teachers, judges and editors — to do the same. The writer currently teaches Expository Writing at AKUFAS. Published in Dawn, May 30th, 2026
Loryn Brantz created The Good Advice Cupcake for BuzzFeed years ago. The company licensed the character for a new Amazon series—made with AI—without her consent.
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May security update trips over hostnames of a very specific length
In response, Jonah Peretti, president of BuzzFeed AI, said that Loryn Brantz's "personal opposition to AI cannot determine how BuzzFeed develops IP that it owns."
A post jocularly characterizing AIs as our children reminded me of this stanza from Philip Larkin: They fuck you up,… The post A Prescient Poem About AI appeared first on Reason.com.
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In some workplace training videos, actors are being replaced by AI. NPR's Scott Simon talks to actor Paul Clayton, who has appeared in more than 1,000 corporate acting roles.
Over the next few decades, billions of autonomous, AI-powered robots will work alongside people in factories, perform tedious tasks in warehouses, care for the elderly, assist in unsafe disaster areas, deliver packages and food to our doorsteps, and eventually help out in our homes. Some will look like us, and many won’t. What is certain is that regardless of form factor, robots will all rely heavily on AI in order to deliver real-world value. In 2025, total investments in robotics companies reached a record US $40.7 billion, accounting for 9 percent of all venture funding. The multibillion dollar question therefore is this: What will it take for AI-powered robots to begin to have a serious economic impact? Many of today’s robotics and AI companies are making bold claims, such as that humanoid robots will soon be coming into our homes, but there’s still a big gap between promise and reality. The promise of robots that live and work alongside us has been the stuff of science fiction for a very long time. And while many programmers have tried to make that promise a reality, the physical world is just too complicated for traditional computer programs to handle the endless complexity it presents. Thanks to AI, robots are no longer being programmed—instead, they learn to operate in the real world. With enough practice, they can learn to perceive and understand the world around them, reason about that world, and use that reason and understanding to perform tasks that are useful, reliable, and safe. The two of us have worked at the forefront of AI and robotics for the last decade, as a Professor in Robotics at Oregon State University and Co-Founder of Agility Robotics, and as former CEO of the Everyday Robots moonshot at Google X. Our experience deploying AI-powered robots in real-world settings has given us a perspective on where AI can be used to great benefit in complex robotic systems in the near term and where we are still on the frontier of science fiction. We believe AI will enable an inflection point in robotics advances, but that it will be through the well-engineered application of coordinated systems of different AI tools rather than a single ChatGPT-style breakthrough. As the excitement around AI is matched only by the uncertainty of what will be possible, here are five hard truths that will define AI in robotics. 1. The YouTube-to-Reality Gap Is Real For years, we have been seeing videos on YouTube with humanoid robots performing amazing moves on everything from a dance floor to an obstacle course. The inside knowledge in robotics is to “never trust a YouTube robot video.” The gap between real robots that can perform real work in unstructured human environments and carefully scripted and edited robot performances remains significant. The latest performance to get a lot of attention was a martial arts show featuring Unitree humanoid robots performing with children at the Chinese 2026 Spring Festival Gala. While impressive, this falls into a long lineage of tightly scripted robotic performances, where everything has been carefully choreographed and planned in advance. The low-level controls, synchronization, and choreography were stunning, yet the Spring Gala robot performance showed a level of autonomy and intelligence much closer to industrial robots building cars in a factory than something that will show up in your living room any time soon. Seeing these kinds of demos nevertheless raises questions about where robotics really is. If robots can perform kung fu moves and do backflips and dance, why aren’t they also showing up on factory floors yet? And why can’t they do the dishes in my home after dinner? The simple answer is this: Making AI-powered robots capable of performing general tasks in varied human environments is still really hard. While impressive technological feats like those at the Spring Festival may make it look like we could be very close, the use of AI in these demos is only for low-level motor control (to keep the robots from falling over) and therefore is only a small part of the solution for robots to be general purpose in the real, unstructured spaces where we humans live and work. 2. Data Is An Unsolved Challenge Large Language Models (LLMs) like OpenAI’s ChatGPT and Anthropic’s Claude were initially trained on an internet-scale database of text. The world woke up one day in late 2022 to ChatGPT demonstrating that AI computers could suddenly “speak” to us in prose or verse and about seemingly any topic. LLMs have turned out to generalize well and are now able to take multimodal input (text, images, video) and produce multimodal output. Importantly, the corpus of training data was both enormous and human-generated, which are characteristics that form the gold standard for AI training. The fastest path to robots as part of everyday life may emerge through a range of robot forms performing increasingly sophisticated applications and employing a range of AI tools.Agility Robotics Giving AI a body (in the form of a robot), so that it can engage with people in the physical world, continues to be a very difficult and broadly unsolved problem. AI models for general-purpose robotics must simultaneously satisfy multiple, often conflicting, physical, geometric, and temporal limitations while operating in unstructured, dynamic environments. In order to generalize, robot models need to be trained on data gathered in a high-dimensional configuration space, where “dimensions” represent text, lighting conditions, degrees of freedom, joint limits, velocities, force, and safety boundaries, just to mention a few. Importantly, this must be good data—it must contain many examples from what amounts to an infinite number of possible configurations in the physical world. Since there are very few existing sources of data like this, approaches like teleoperation, video analysis, motion capture of humans, and self-exploration in simulation and in the real world are all seen as important ways to collect data. It’s a herculean task. For example, at Everyday Robots at Google X, we ran 240 million robot instances in our simulator over the course of 2022 to collect training data, mostly to train a trash-sorting model. Similar amounts of data will be needed for every skill to get to a similar level of capability, which is not yet human level. 3. There Will Be No Single Robot AI We are far away from a moment where a single AI model might allow general-purpose robots to live and work alongside us. General-purpose robots can have wheels or legs. They can have one, two, three, or more arms. Some have propellers and can fly, while others may be designed to operate under water. Some will drive on busy roads. The physical world is infinitely varied and complex. And then there are all the people and other animals that will be surrounding the robots. How do you train a model to operate a robot safely and reliably in all of these settings? The simple answer is: You don’t. At least not for quite some time. We believe the winning AI architecture leading to the next big breakthroughs in general-purpose robotics will be “agentic AI” for robots, which are high-level coordinating models that can reason, plan, use tools, and learn from outcomes to execute complex tasks with limited supervision. Agentic, high-level models running on robots will invoke a system of specialized ones for different types of tasks. We will likely soon see multiple robots collaborating and coordinating with each other through their onboard agentic AI models. AI tools are unlocking new and powerful capabilities in robotics, which in turn will enable new solutions and new markets. It’s encouraging to see these new models being made broadly available, some even as open-source solutions. This availability is akin to what happened with the internet: Real progress occurred when it became ubiquitous. We anticipate an inevitable democratization of complex behaviors in robotics with wide access to these AI tools and technologies. 4. Hardware Is Still Very Hard Robots are complex systems with many parts that all need to work together with great precision. For a robot to be useful and safe, every part of it must be coordinated, from its perception systems to the computer controlling it, all the way down to its individual actuators. Actuators—that is, the motors and gears—are a good example of an important part of the robot where what got us here won’t get us there. The actuators used at scale by most industrial robots will not work for robots that will operate in human environments. If these robots accidentally collide with an obstacle, the resulting impacts are harsh, forces are high, and things break. Humans don’t move in this way. We are far more compliant in how we interact with the world, and we’re constantly making contact with our environment and using that contact to help us accomplish things. Consider the challenge of inserting a key in a lock: Humans typically don’t do this by aligning the key perfectly with the keyhole. Instead, we just feel for the edge of the keyhole and jiggle the key in. Robots need to be able to operate in novel ways to achieve comparable capabilities by using a new class of actuators that are sensitive to force and able to have a compliant interaction with the environment. While these kinds of actuators do exist, they are not yet generally available at scale for robot systems designed to operate around people. 5. Real Value Comes From “Easy” Tasks There’s a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of Moravec’s paradox, which states that tasks that are hard for humans are easy for computers (like multiplying two big numbers), and tasks easy for humans (like a toddler’s movements) are extremely difficult for computers and robots. Serving customers is an unforgiving reality check, because customers only care about solving the real problems they have. If we are to deploy AI-based robot solutions, they must outperform the way things are currently done while demonstrating reliable performance metrics and safety. Agility Robotics’ early work to deploy our humanoid robot Digit in customer locations led to the realization that our first obstacle was safety: Robots that balance and manipulate objects in human spaces bring new types of risk to the workplace. In the first humanoid deployments, physical barriers were necessary, and Agility kicked off a multi-year engineering effort to solve the safety challenge, touching nearly every aspect of robot design and relying heavily on new AI-based approaches to human detection and behavior control. Everyday Robots at Google deployed robots in 2019 that worked autonomously in office buildings doing chores like cleaning cafe tables and sorting trash. We quickly learned how “messy” and difficult the real world is for a robot. This experience informed the architecture and deployment of our AI systems while also gathering real-world data that could be combined with simulation data for training and improving models. This focus on creating a product to meet specific customer needs and deploying robots in real-world settings is the only way to inform the structure of the AI tools and infrastructure for near-term utility on a path towards long-term broader capability and generality. There will be no “aha” moment, no silver bullet algorithm, and no volume of data sufficient to produce a general-purpose robot without extensive real-world experience. AI Robots Are Coming, One Step at a Time As we look to the future, there is no doubt that the world is bringing AI into the physical world through robots. We are at the beginning of a “Cambrian explosion“ of useful, intelligent machines. We believe AI is not one tool, but a huge frontier of technical approaches that is unlocking new capabilities so powerful, they will define our economy moving forward. This will happen not in one single definitive moment, but as an ongoing set of small and large breakthroughs, where AI-driven robots begin to provide real value in a few tasks, and then a few more, with impacts unfolding across numerous $100 billion-plus markets that will dramatically improve the quality of our lives.
For years, the field of robotics has used the terms “dull, dirty, and dangerous” (DDD) to describe the types of tasks or jobs where robots might be useful—by doing work that’s undesirable for people. A classic example of a DDD job is one of “repetitive physical labor on a steaming hot factory floor involving heavy machinery that threatens life and limb.” But determining which human activities fit into these categories is not as straightforward as it seems. What exactly is a “dull” task, and who makes that assumption? Is “dirty” work just about needing to wash your hands afterwards, or is there also an aspect of social stigma? What data can we rely on to classify jobs as “dangerous?” Our recent work (which was not dull at all) tackles these questions and proposes a framework to help roboticists understand the job context for our technology. First, we did an empirical analysis of robotics publications between 1980 and 2024 that mention DDD and found that only 2.7 percent define DDD and only 8.7 percent provide examples of tasks or jobs. The definitions vary, and many of the examples aren’t particularly specific (for example, “industrial manufacturing,” “home care”). Next, we reviewed the social science literature in anthropology, economics, political science, psychology, and sociology to develop better definitions for “dull,” “dirty,” and “dangerous” work. Again, while it might seem intuitive which tasks to put into these buckets, it turns out that there are some underlying social, economic, and cultural factors that matter. Dangerous Work: Occupations or tasks that result in injury or risk of harm It’s possible to measure the danger of a task or job by using reported information. There are administrative records and surveys that provide numbers on occupational injury rates and hazardous risk factors. While that seems straightforward, it’s important to understand how this data was collected, reported, and verified. First, occupational injuries tend to be underreported, with some studies estimating up to 70 percent of cases missing in administrative databases. Second, injuries and risk factors are rarely disaggregated by characteristics like gender, migration status, formal/informal employment, and work activities. For example, because most personal protective equipment—such as masks, vests, and gloves—are sized for men, women in dangerous work environments face increased safety risks. These caveats are an opportunity for robotics to be helpful. If we went out and looked for it, we could probably find some less obviously dangerous work where robotics might be an important intervention, not to mention some groups that are disproportionately affected and would benefit from more workplace safety. Dirty Work: Occupations or tasks that are physically, socially, or morally tainted Colloquially, most people might think of dirty work as involving physical dirtiness, such as trash removal, cleaning, or dealing with hazardous substances. But social science literature makes clear that dirty work is also about stigma. Socially tainted jobs are often servile or involve interacting with stigmatized groups (for example, correctional officers), and morally tainted jobs include tasks that people commonly perceive as sinful, deceptive, or otherwise defying norms of civility (like a stripper or a collection agent). “Dirty work” is a social construct that can vary across time (like tattoo industry stigma in the United States) and culture (such as nursing in the U.S. versus in Bangladesh). One way to measure whether work is “dirty” is by using the closely related concept of occupational prestige, captured through quantitative surveys where people rank jobs. Another way to measure it is through qualitative data, like ethnographies and interviews. Similar to “dangerous,” we see some hidden opportunities for robotics in “dirty” work. But one of our more interesting takeaways from the data is that a lower-ranked job can be something that the workers themselves enjoy or find immense pride and meaning in. If we care about what tasks are truly undesirable, understanding this worker perspective is important. Dull Work: Occupations or tasks that are repetitive and lacking in autonomy When it comes to defining dull work, what matters most is workers’ own experiences. Outsiders can make a lot of false assumptions about what tasks have value and meaning. Sometimes things that seem boring or routine create the right conditions for developing skills and competence, such as the concentration needed for woodworking, or for socializing and support, when tasks are done alongside others. Instead of assuming that repetitive work is negative, it’s important to examine qualitative data on how people experience the work and what purpose it serves for them. DDD: An actionable framework In our paper, we propose a framework to help the robotics community explore how automation impacts individual jobs. For each term—dull, dirty, and dangerous—the framework gathers key pieces of information to reflect on what physical or social aspects of the task are, in fact, DDD. Worker perspective is an important part of all three considerations. The framework also emphasizes awareness of context—meaning the physical and social environment of an occupation and industry that can influence the DDD nature of a task. Our corresponding worksheet suggests existing data sources to draw on and encourages us to seek out multiple perspectives and consider potential sources of bias in the information. What makes tasks dull, dirty, or dangerous depends on the perspective of the humans doing those tasks.RAI Let’s take, for example, the waste and recycling industry. The world generates over 2 billion tonnes of waste annually, and this figure is expected to rise to nearly 4 billion tonnes by 2050. Intuitively, trash collection seems like a job that hits all the Ds. Going through our worksheet, we confirm that globally, workers in this industry face significant health hazards (dangerous), and waste collection is ranked as a low-status job (dirty), although interestingly, many workers take pride in providing this essential service. The job is also repetitive, but there are aspects that make it not dull. Specifically, workers cite the day-to-day interaction with their coworkers (which includes extensive insider vocabulary, work hacks, and mutual aid groups) and task variety as two of the most enjoyable aspects of the job. Task variety includes inspecting their vehicle and equipment, driving their truck, coordinating with crew members, lifting bins and bags, detecting incorrect sorting of waste, and unloading at the end destination. This finding matters because some types of robotic solutions will eliminate the parts of the job that workers most appreciate. For instance, the National Institute for Occupational Safety and Health (NIOSH) recommends the adoption of automated side loader trucks and collision avoidance systems. This innovation increases safety, which is great, but it also results in a sole worker operating a joystick in a cab, surrounded by sensor and camera surveillance. Instead, we should challenge ourselves to think of solutions that make jobs safer without making them terrible in a different way. To do this, we need to understand all aspects of what makes a job dull, dirty, or dangerous (or not). Our framework aims to facilitate this understanding. Finally, it’s important to note that DDD is only one of many possible approaches to classify what work might be better served by robots. There are lots of ways we could think about which types of tasks or jobs to automate (for example, economic impact or environmental sustainability). Given the popularity of DDD in robotics, we chose this common phrase as a starting point. We would love to see more work in this space, whether it’s data collection on DDD itself or the creation of other frameworks. At RAI, we believe that the fusion of robotics and social sciences opens a whole new world of information, perspectives, opportunities, and value. It fosters a culture of curiosity and mutual learning, and allows us to create actionable tools for anyone in robotics who cares about societal impact. Dull, Dirty, Dangerous: Understanding the Past, Present, and Future of a Key Motivation for Robotics, by Nozomi Nakajima, Pedro Reynolds-Cuéllar, Caitrin Lynch, and Kate Darling from the RAI Institute, was presented at the 21st ACM/IEEE International Conference on Human-Robot Interaction (HRI) in Edinburgh, Scotland.
This sponsored article is brought to you by Ampace. As AI workloads grow to gigascale levels, the global data center industry has hit a hidden physical wall. The real bottleneck is no longer just the thermal limit of the chip or the capacity of the cooling system — it is the dynamic resilience of the power chain. Modern AI computing clusters, driven by massive GPU clusters, generate high-frequency, abrupt, and synchronized spikey pulse loads. As rack densities soar beyond 100 kW, these fluctuations are amplified into a “power paradox”: while the digital logic of AI is moving faster than ever, the physical infrastructure supporting it remains tethered to legacy response capabilities. The power usage of these gigascale sites and their drastic, high frequency, abrupt load surges from the AI GPU clusters can trigger transient voltage events and frequency instability, risking the entire local grid. The grid itself is not robust enough to support these loads. This leads to the infrastructure gap: The utility is not robust enough and traditional backup sources, such as diesel generators and gas turbines, simply cannot react to millisecond-level power spikes in output. This will often force operators into a cycle of costly infrastructure over sizing just to buffer the volatility. AI infrastructure requires energy systems capable of instantaneous response while safeguarding continuity and reliability. The industry has explored various mitigations — from rack-level BBUs to 800V DC architectures — yet the mature, high volume, traditional UPS system remains the most viable and scalable foundation for gigawatt-level facilities. Consequently, the UPS-integrated battery system has emerged as the critical “physical buffer” to neutralize these pulses at the source. At Data Center World 2026 in Washington, D.C., Ampace led a pivotal technical dialogue with Eaton during the session “Powering Giga-scale AI.” Their exchange unveiled a fundamental paradigm shift: To bridge the AI power gap, energy storage must evolve from a passive insurance policy into an active, high-speed stabilizer. By aligning Ampace’s semi-solid-state battery innovation with Eaton’s proven system intelligence, we are moving beyond simple backup to solve the physical paradox of the AI era. To move beyond simple backup and solve the physical paradox of the AI era, Ampace is aligning its semi-solid-state battery innovation with Eaton’s proven system intelligence.Ampace The “Shock Absorber” physics: semi-solid chemistry for AI pulses Conventional power systems were designed for steady-state loads, not the rapid heartbeat of a massive AI GPU cluster. When thousands of GPUs synchronize their computing cycles, they generate high-frequency, abrupt pulse loads that can lead to voltage sags, frequency oscillations, and potential interruptions of critical AI training. Ampace’s PU Series semi-solid and low-electrolyte cells address this challenge by acting as high-speed “shock absorbers.” Leveraging ultra-low internal resistance (DCR) and high cycle capability, these batteries neutralize millisecond-level power spikes at the source, stabilizing the local power loop before disturbances propagate upstream to the grid or on-site generators. These high-rate cells enable 100 kW+ racks to maintain peak performance without transmitting instability across the power chain. This capability aligns closely with Eaton’s matured UPS architectures, such as double-conversion topologies and advanced power electronics upgrades, which have long prioritized rapid load responsiveness and high system stability. Together, these approaches embody a shared industry philosophy: AI infrastructure requires energy systems capable of instantaneous response while safeguarding continuity and reliability. Ampace’s semi-solid state chemistry minimizes liquid electrolyte, greatly reducing the risk of leakage and thermal runaway under continuous AI high-load conditions.Ampace Algorithmic intelligence: synchronizing energy and control Hardware alone cannot solve the AI power paradox; the system also requires intelligent coordination between energy storage and power management. Sophisticated battery management systems (BMS) like Ampace’s high-precision design track state-of-charge (SOC) with high-speed sampling, even during rapid, shallow cycling typical in AI workloads. Complementary algorithmic approaches in modern UPS platforms — such as ramp-rate control and average power management — effectively suppress sub-synchronous oscillations and optimize load smoothing. In large-scale AI training environments, where thousands of GPUs can trigger millisecond-level power pulses, these intelligent layers ensure that batteries buffer high-frequency fluctuations without compromising the mandatory emergency backup reserves. By transforming energy storage from passive “standby insurance” into active, schedulable assets, the system simultaneously safeguards continuous AI training and maintains the long-term health of the data center infrastructure. In practical terms, this means that even during peak compute bursts, the infrastructure remains stable, training cycles continue uninterrupted, and operators avoid costly oversizing or grid stress. Eaton’s dual-layer algorithms serve as a valuable benchmark in this space, demonstrating how advanced control logic can achieve similar objectives, reinforcing Ampace’s approach and philosophy within the broader data center power ecosystem. Economic scalability: optimizing AI infrastructure efficiently One of the largest costs in deploying AI infrastructure is “oversizing”: procuring transformers, generators, and UPS systems to handle brief peak spikes. This traditional approach inflates the Total Cost of Ownership (TCO) and leads to wasted capital on underutilized hardware. Ampace’s turn-key cabinet design developed by its independent R&D is engineered for seamless compatibility with mature, high volume UPS systems. By leveraging Eaton’s double-conversion UPS topologies alongside intelligent ramp-rate and average power management algorithms, AI data centers can scale dynamically without requiring costly infrastructure redesigns. This approach allows the UPS and batteries to act as active load-shapers, smoothing AI-driven pulses while strictly maintaining mandatory emergency backup capacity. By utilizing energy storage as an active, schedulable asset, operators can right-size their infrastructure, avoid unnecessary grid upgrades, and deploy gigascale AI clusters with unprecedented efficiency. Safety First: Protecting AI Infrastructure While Enabling Innovation In high-density AI facilities, safety is non-negotiable. Ampace’s semi-solid state chemistry minimizes liquid electrolyte, greatly reducing the risk of leakage and thermal runaway under continuous AI high-load conditions. Ampace’s turn-key cabinet design developed by its independent R&D is engineered for seamless compatibility with mature, high volume UPS systems. Ampace At the same time, Eaton’s UPS design emphasizes system-level energy scheduling that never sacrifices mandatory emergency backup reserves, ensuring thermal safety and uninterrupted operation. This “safety-first” approach ensures that infrastructure can sustain aggressive performance targets without compromising the physical integrity of the facility. Coupled with over a decade of proven high-cycle life operation and design under shallow pulse conditions, these systems can extend operational lifespan, reduce replacement requirements, and provide operators with confidence that safety and reliability remain uncompromised as compute density continues to grow. To remain the scalable backbone of AI data centers As AI computing scales over the next two to three years, the industry will face stricter grid requirements and even more demanding pulse load characteristics. This evolution demands a forward-looking design philosophy that harmonizes UPS, battery, and grid compatibility. Ampace views current low-electrolyte semi-solid technologies as the optimal transitional step toward a fully solid-state future — one that promises ultimate safety and performance. Ampace remains committed to this long-term technological roadmap. We view current low-electrolyte semi-solid technologies as the optimal transitional step toward a fully solid-state future — one that promises ultimate safety and performance. Whether through rack-level BBU, integrated UPS systems, or containerized storage, the universal core of the AI era remains constant: high-speed response, long shallow-cycle life, and refined energy management. By engaging in deep technical exchanges with Eaton and leading energy innovators, Ampace ensures that its solutions not only meet today’s AI pulse challenges but also harmonize with broader infrastructure strategies and shared industry best practices. Ultimately, as traditional diesel generators gradually give way to diversified alternatives, the integrated UPS-plus-energy-storage system will become the fundamental infrastructure standard. The dialogue has just begun. Ampace will continue to engage in strategic exchanges with global industrial automation leaders and digital energy pioneers, co-authoring the playbook for a safer, more efficient, and more resilient AI-ready world.
When Ana Inês Inácio goes to work at the Netherlands Organization for Applied Scientific Research (TNO) in The Hague, she thinks about signals most people never notice: radio waves moving between satellites, sensors, and future wireless networks. The integrated circuits the research scientist designs lay the foundation for next-generation RF sensor systems critical to advancing radar technologies. Ana Inês Inácio EMPLOYER Netherlands Organization for Applied Scientific Research, TNO TITLE Scientist IEEE MEMBER GRADE Senior member ALMA MATER University of Aveiro, in Portugal Those invisible RF signals are only part of what earned the IEEE senior member her global recognition. Inácio recently received the IEEE–Eta Kappa Nu Outstanding Young Professional Award for “leadership in IEEE Young Professionals, fostering innovation and inclusivity, and pioneering advancements in RF sensor systems, bridging technical excellence with impactful community engagement.” The recognition from IEEE’s honor society reflects a career built along two parallel paths: advancing RF circuit design while helping engineers worldwide build professional communities. “I’ve always liked building things,” Inácio says. “Sometimes that means circuits; sometimes it means helping people connect and grow together.” That blend of technical innovation and global leadership gives her work impact far beyond the laboratory. EE lessons at the kitchen table Inácio grew up in Vales do Rio, a rural village near Covilhã in central Portugal. The region was known for farming and textiles, she says. Many residents worked in the textile industry, including her grandfather, who repaired machinery such as industrial looms. He became her first engineering teacher without ever holding the formal title. Through correspondence courses delivered by mail, he taught himself electrical systems. At home, he explained electricity to his granddaughter while he repaired the household’s appliances and wiring. “He would show me why something broke and how we could fix it,” she recalls. It sparked her curiosity. Her mother was a tailor who later managed other tailors. Her father left his factory job to attend culinary school and now cooks at an elder-care facility. Curiosity was a trait that ran through the family. By high school, Inácio was drawn equally to mathematics and physics and to biology and geology, she says. Encouragement from teachers and an uncle, an engineer, ultimately steered her toward electronics engineering. Conducting research on integrated circuits In 2008 she enrolled in an integrated master’s degree program in electrical and telecommunications engineering at the Universidade de Aveiro in Portugal, a five-year degree that combined undergraduate and graduate studies. An opportunity to study abroad changed her path. In 2012 she moved to the Netherlands to study at Eindhoven University of Technology (TU/e) through a six-month European exchange program with UAveiro. A professor encouraged her to stay on, so she completed her final year of masters in the Netherlands. She focused on techniques to improve the linearization of RF power amplifiers at Thales. The company, based in Hengelo, Netherlands, designs and produces electronics for defense and security. She earned her master’s degree from UAveiro in 2013. After graduating, she joined the integrated circuit design group at the University of Twente, in The Netherlands, conducting collaborative research as part of a nationally funded program on linearization techniques for RF front-end systems. The experience introduced her to international research culture and persuaded her to pursue a career abroad, she says. Engineering the future of wireless Inácio joined TNO in 2018 as a junior scientist and innovator: her first professional industry job. Today she designs integrated RF front-end systems—the circuits that allow devices to transmit and receive wireless signals. The components sit at the core of modern communications, enabling sensor networks, satellite links, and emerging 6G technologies. Her work aims to tackle a central challenge: getting greater performance from smaller chips. “As communication evolves, we need more bandwidth to transfer more data at higher speeds,” she says. “The question is how much complexity you can integrate into one system while keeping it efficient.” Unlike commercial lab environments, which reuse established designs, research projects often start from scratch. Each transmit-receive chain—the signal path that converts digital data to radio waves and back again—is tailored to specific requirements. Her work focuses on improving key circuit characteristics including linearity (ensuring that the signals that go out of the antenna are not distorted) as well as noise reduction (so design blocks can be optimized). Advanced design techniques help devices communicate more reliably while consuming less energy, a critical need for large sensor networks such as the Internet of Things, she says. Artificial intelligence is beginning to influence her field, she says: “AI is already helping us work faster. The real challenge is learning how to use it to make better designs, not just quicker ones.” A parallel vocation with IEEE While her technical career flourished in research labs, an additional journey unfolded through IEEE. Inácio joined the organization in 2009 as a student after discovering UAveiro’s student branch. What began as curiosity evolved into a long-term leadership path. She advanced through roles within Region 8—covering Europe, Africa, and the Middle East—one of the organization’s most culturally diverse regions. She was the student branch’s vice chair, and the region’s student representative for more than 22,000 IEEE members. She also served as the Young Professionals Affinity Group chair for the IEEE Benelux Section, which encompasses Belgium, the Netherlands, and Luxembourg. Currently, she serves as the immediate past chair of the Region 8 Young Professionals Committee, and vice chair and IEEE Member and Geographical Activities representative on the IEEE Young Professionals Committee. In those roles, she represents close to 135,000 IEEE members. In addition, she is an active member of the IEEE Microwave Theory and Technology Society, currently serving as its Young Professionals liaison. Her involvement with IEEE has boosted her professional confidence, she says. “IEEE didn’t directly give me promotions at my day job, but it gave me leadership skills, networking opportunities, and the ability to work with people from everywhere,” she says. Those experiences now shape her collaborations at TNO, where international teamwork is essential. The IEEE-HKN Outstanding Young Professional Award recognizes that combination of technical excellence and community impact, she says. Looking back, Inácio sees a clear thread connecting her childhood curiosity, her international career, and her IEEE leadership: Engineering, she says, is ultimately about people as much as it is about technology.