technology 24 for you

Thursday, May 21, 2026

US government takes $2 billion equity stake in nine quantum computing firms

The US government will take equity stakes worth a total of $2 billion in a slew of quantum computing companies, including a startup backed by a firm with links to the Trump family and one taken public by a Pentagon official. The announcement by the commerce department that it had signed letters of intent with nine companies—including GlobalFoundries and IBM—sent shares in quantum specialists soaring on Thursday. Both IBM, which is set to get $1 billion, and GlobalFoundries, which will receive $375 million, were up more than 6 percent in pre-market trading. D-Wave Quantum, an awardee that was taken public in 2022 by Emil Michael—now a top Pentagon official—was up more than 20 percent.<a href="https://arstechnica.com/gadgets/2026/05/us-government-takes-2-billion-equity-stake-in-nine-quantum-computing-firms/">Read full article</a> <a href="https://arstechnica.com/gadgets/2026/05/us-government-takes-2-billion-equity-stake-in-nine-quantum-computing-firms/#comments">Comments</a> Reference : https://ift.tt/zFUTfNL

SEM-Guided Low-kV FIB Finishing for Leading-Edge Semiconductor Failure Analysis

<img src="https://spectrum.ieee.org/media-library/zeiss-logo-above-the-slogan-seeing-beyond-on-a-dark-curved-rectangle.png?id=66728517&width=980"/> Discover how the ZEISS Crossbeam 750 FIBSEM sets a new benchmark for precise TEM lamella prep, tomography, and advanced nanofabrication. This delivers better resolution, better SNR, larger usable FOV, and shorter acquisition times. Learn how uninterrupted FIB milling will reduce damage and rework, accelerate time to TEM, and increase first pass success—so your FA, yield, and materials teams make faster, confident data driven decisions.Join us to discover how the new ZEISS Crossbeam 750 with its see while you mill capability delivers precision and clarity—every time—for demanding FIB-SEM workflows. Designed for extremely challenging TEM lamella preparation, tomography, advanced nanofabrication, and APT‑ready lift‑out, Crossbeam 750 combines a new Gemini 4 SEM objective lens, a double deflector, and a next‑generation scan generator to elevate both image quality and process confidence. You’ll learn how better resolution and better SNR translate into more image detail and shorter acquisition times, while the low‑kV FIB performance enables more precise lamella prep.We’ll demonstrate High Dynamic Range (HDR) Mill + SEM—an interwoven SEM/FIB scanning mode that suppresses FIB‑generated background. This enables immediate, clean visual feedback, even during nudging the FIB pattern live while milling . The result: confident endpointing with uninterrupted FIB milling and pristine, metrology‑grade surfaces with the lowest possible sample damage. This session is ideal for semiconductor failure analysists, yield teams and materials scientists seeking faster time‑to‑TEM, higher first‑pass success, and consistent outcomes at low kV. See how Crossbeam 750 empowers you to make earlier stop‑milling decisions, cut rework, and reliably plan turnaround time—so you can move from sample to insight with confidence.<a href="https://events.bizzabo.com/868497/home" target="_blank">Register now for this free webinar!</a> Reference: https://events.bizzabo.com/868497/home

Wednesday, May 20, 2026

Google publishes exploit code threatening millions of Chromium users

Google on Wednesday published exploit code for an unfixed vulnerability in its Chromium browser codebase that threatens millions of people using Chrome, Microsoft Edge, and virtually all other Chromium-based browsers. The proof-of-concept code exploits the Browser Fetch programming interface, a standard that allows long videos and other large files to be downloaded in the background. An attacker can use the exploit to create a connection for monitoring some aspects of a user’s browser usage and as a proxy for viewing sites and launching denial-of-service attacks. Depending on the browser, the connections either reopen or remain open even after it or the device running it has rebooted. <h2>Unfixed for 29 months (and counting)</h2> The unfixed vulnerability can be exploited by any website a user visits. In effect, a compromise amounts to a limited backdoor that makes a device part of a limited botnet. The capabilities are limited to the same things a browser can do, such as visit malicious sites, provide anonymous proxy browsing by others, enable proxied DDoS attacks, and monitor user activity. Nonetheless, the exploit could allow an attacker to wrangle thousands, possibly millions, of devices into a network. Once a separate vulnerability becomes available, the attacker could use it to then compromise all those devices.<a href="https://arstechnica.com/security/2026/05/google-publishes-exploit-code-threatening-millions-of-chromium-users/">Read full article</a> <a href="https://arstechnica.com/security/2026/05/google-publishes-exploit-code-threatening-millions-of-chromium-users/#comments">Comments</a> Reference : https://ift.tt/Pj3Nyid

Will Robotics Have a ChatGPT Moment?

<img src="https://spectrum.ieee.org/media-library/a-collection-of-5-robots-against-colored-backgrounds.jpg?id=66734221&width=1245&height=700&coordinates=0%2C88%2C0%2C88"/> 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, <a href="https://spectrum.ieee.org/collections/darpa-subterranean-challenge/" target="_blank">assist in unsafe disaster areas</a>, 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.<div class="rm-embed embed-media"><iframe height="110px" id="noa-web-audio-player" src="https://embed-player.newsoveraudio.com/v4?key=q5m19e&id=https://spectrum.ieee.org/robotics-ai-breakthrough?draft=1&bgColor=F5F5F5&color=1b1b1c&playColor=1b1b1c&progressBgColor=F5F5F5&progressBorderColor=bdbbbb&titleColor=1b1b1c&timeColor=1b1b1c&speedColor=1b1b1c&noaLinkColor=556B7D&noaLinkHighlightColor=FF4B00&feedbackButton=true" style="border: none" width="100%"></iframe></div>In 2025, total investments in robotics companies reached <a href="https://www.cbinsights.com/research/report/venture-trends-2025/" target="_blank">a record $40.7 billion, accounting for 9 percent of all venture funding</a>. 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 <a href="https://www.1x.tech/" target="_blank">soon be coming into our homes</a>, 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 <a href="https://engineering.oregonstate.edu/people/jonathan-hurst" target="_blank">Professor in Robotics at Oregon State University</a> and <a href="https://www.agilityrobotics.com/about/leadership" rel="noopener noreferrer" target="_blank">Co-Founder of Agility Robotics</a>, and as <a href="https://www.linkedin.com/in/hanspeter/" rel="noopener noreferrer" target="_blank">former CEO</a> of the <a href="https://everydayrobots.ai/" rel="noopener noreferrer" target="_blank">Everyday Robots moonshot at Google X</a>. 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.<h2>1. The YouTube-to-Reality Gap Is Real</h2>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 <a href="https://www.youtube.com/watch?v=mUmlv814aJo" rel="noopener noreferrer" target="_blank">martial arts show</a> 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. <iframe frameborder="0" height="auto" lazy-loadable="true" scrolling="no" src="https://www.youtube.com/embed/mUmlv814aJo?rel=0" style="position:absolute;top:0;left:0;width:100%;height:100%;" width="100%"></iframe> 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.<h2>2. Data Is An Unsolved Challenge</h2>Large Language Models 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. <img alt="A series of four images, including robots working in a contained factory space, in an open indoor factory, outdoors in the real world delivering a package, and working with a human to move a couch in an apartment." class="rm-shortcode" data-rm-shortcode-id="f8dd8681a93fee0b55cfebeab420789a" data-rm-shortcode-name="rebelmouse-image" id="a0903" loading="lazy" src="https://spectrum.ieee.org/media-library/a-series-of-four-images-including-robots-working-in-a-contained-factory-space-in-an-open-indoor-factory-outdoors-in-the-real.jpg?id=66734272&width=980"/> 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 RoboticsGiving 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.<h2>3. There Will Be No Single Robot AI</h2>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 on-board 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.<h2>4. Hardware Is Still Very Hard</h2>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.<h2>5. Real Value Comes From “Easy” Tasks</h2>There’s a big difference between tasks that look impressive and real-world tasks that provide value. Robotics is a perfect example of <a href="https://en.wikipedia.org/wiki/Moravec%27s_paradox" target="_blank">Moravec’s paradox</a>, 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 <a href="https://www.youtube.com/watch?v=AJpTpUqjgrY" target="_blank">humanoid deployments</a>, 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. <iframe frameborder="0" height="auto" lazy-loadable="true" scrolling="no" src="https://www.youtube.com/embed/E2g1APtSuUM?rel=0" style="position:absolute;top:0;left:0;width:100%;height:100%;" width="100%"></iframe> <a href="https://everydayrobots.ai/vision" target="_blank">Everyday Robots</a> 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. <h2>AI Robots Are Coming, One Step at a Time</h2>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 “<a href="https://spectrum.ieee.org/is-a-cambrian-explosion-coming-for-robotics" target="_self">Cambrian explosion</a>“ 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. Reference: https://ift.tt/vrcMzAg

Tuesday, May 19, 2026

In stunning display of stupid, secret CISA credentials found in public GitHub repo

Security researcher Brian Krebs <a href="https://krebsonsecurity.com/2026/05/cisa-admin-leaked-aws-govcloud-keys-on-github/">brings us the news</a> that America's <a href="https://www.cisa.gov/">Cybersecurity & Infrastructure Agency</a> (CISA) has had a large store of plaintext passwords, SSH private keys, tokens, and "other sensitive CISA assets" exposed in a public GitHub repo since at least November 2025. The now-offline public repo—named, somewhat aspirationally, "Private-CISA"—was brought to Krebs' attention by GitGuardian's <a href="https://blog.gitguardian.com/author/guillaumevaladon/">Guillaume Valadon</a>, who was alerted to the repo's presence by GitGuardian's public code scans. Krebs says that Valadon approached him after receiving no responses from the Private-CISA repo's owner. In an email to Krebs, Valadon claimed that the repo's commit logs show that GitHub's default protections against committing secrets—protections designed to protect unwitting or unskilled developers against exactly this kind of stupidness—had been disabled by the repo's administrator.<a href="https://arstechnica.com/information-technology/2026/05/in-stunning-display-of-stupid-secret-cisa-credentials-found-in-public-github-repo/">Read full article</a> <a href="https://arstechnica.com/information-technology/2026/05/in-stunning-display-of-stupid-secret-cisa-credentials-found-in-public-github-repo/#comments">Comments</a> Reference : https://ift.tt/ZKpS56r

Monday, May 18, 2026

What Makes a Job Dull, Dirty, or Dangerous?

<img src="https://spectrum.ieee.org/media-library/a-curbside-trash-can-being-lifted-by-a-mechanical-arm-attached-to-the-side-of-a-garbage-truck.jpg?id=66736070&width=1245&height=700&coordinates=0%2C178%2C0%2C178"/> 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 <a href="https://dl.acm.org/doi/10.1145/1349822.1349827" rel="noopener noreferrer" target="_blank">classic example of a DDD job</a> 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?” <a href="https://rai-inst.com/resources/papers/dull-dirty-dangerous-understanding-the-past-present-and-future-of-a-key-motivation-for-robotics/" target="_blank">Our recent work</a> (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% define DDD and only 8.7% provide examples of tasks or jobs. The definitions vary, and many of the examples aren’t particularly specific (e.g., “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.<h3>Dangerous Work: Occupations or tasks that result in injury or risk of harm</h3>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 these data were collected, reported, and verified.First, occupational injuries tend to be underreported, with some studies estimating <a href="https://pubmed.ncbi.nlm.nih.gov/24507952/" target="_blank">up to 70% of cases missing in administrative databases</a>. Second, injuries and risk factors are <a href="https://www.ilo.org/publications/quick-guide-sources-and-uses-statistics-occupational-safety-and-health" target="_blank">rarely disaggregated by characteristics like gender, migration status, formal/informal employment, and work activities</a>. For example, because most personal protective equipment, such as masks, vests, and gloves, are sized for men, <a href="https://books.google.com/books?hl=en&lr=&id=GdmEDwAAQBAJ&oi=fnd&pg=PT8&dq=Caroline+Criado+Perez.+Invisible+Women:+Data+Bias+in+a+World+Designed+for+Men.+Vintage+Books,+New+York,+NY,+2019.+ISBN+1-68335-314-5.+&ots=zr92hEL4HB&sig=bepPAzAfk_khTOb8BO6xWjspDJM#v=onepage&q&f=false" target="_blank">women in dangerous work environments face increased safety risks</a>.These caveats are an opportunity for robotics to be helpful: If we went out and looked for it, we could probably find some less obvious 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.<h3>Dirty Work: Occupations or tasks that are physically, socially, or morally tainted</h3>Colloquially, most people might think of dirty work as involving physical dirtiness, like trash, cleaning, or hazardous substances But social science literature makes clear that dirty work is <a href="https://www.jstor.org/stable/799402" target="_blank">also about stigma</a>. Socially tainted jobs are often servile or involve interacting with stigmatized groups (e.g., correctional officer) and morally tainted jobs include tasks that people commonly perceive as sinful, deceptive, or otherwise defying norms of civility (e.g., stripper, collection agent.)“Dirty work” is a social construct that can vary across time (like <a href="https://psycnet.apa.org/record/2012-00729-001" target="_blank">tattoo industry stigma</a> in the US) and culture (such as nursing in the <a href="https://link.springer.com/chapter/10.1057/9780230393530_8" target="_blank">US</a> vs. in <a href="https://www.sciencedirect.com/science/article/abs/pii/S0277953606003418" target="_blank">Bangladesh</a>). 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 <a href="https://www.jstor.org/stable/259134" target="_blank">the workers themselves enjoy or find immense pride and meaning in</a>. If we care about what tasks are truly undesirable, understanding this worker perspective is important.<h2>Dull Work: Occupations or tasks that are repetitive and lacking in autonomy</h2>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 <a href="https://www.penguinrandomhouse.com/books/291654/the-mind-at-work-by-mike-rose/" rel="noopener noreferrer" target="_blank">developing skills and competence</a>, such as the concentration needed for woodworking, or for <a href="https://www.anthropology-news.org/articles/what-counts-as-drudgery-and-who-decides/" rel="noopener noreferrer" target="_blank">socializing and support</a>, 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.<h3>DDD: An actionable framework </h3>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, i.e. the physical and social environment of an occupation and industry that can influence the DDD nature of a task. Our corresponding <a href="https://arxiv.org/pdf/2602.04746" rel="noopener noreferrer" target="_blank">worksheet</a> suggests existing data sources to draw on, as well as encouraging us to seek out multiple perspectives and consider potential sources of bias in the information. <img alt="A diagram illustrating that tasks that are dangerous, dirty, or dull depend on how the workers feel about the social and physical environment." class="rm-shortcode" data-rm-shortcode-id="0e5225e853b1fd8d456f6ae58d665e04" data-rm-shortcode-name="rebelmouse-image" id="ff883" loading="lazy" src="https://spectrum.ieee.org/media-library/a-diagram-illustrating-that-tasks-that-are-dangerous-dirty-or-dull-depend-on-how-the-workers-feel-about-the-social-and-physica.png?id=66736573&width=980"/> What makes tasks dull, dirty, or dangerous depends on the perspective of the humans doing those tasks.RAILet’s take, for example, the waste and <a href="https://spectrum.ieee.org/single-stream-recycling" target="_blank">recycling industry</a>. The world generates over 2 billion tons of waste annually, and this figure is <a href="https://openknowledge.worldbank.org/entities/publication/ba7feea4-0abe-59fb-bc60-ce6b60eb1ceb" target="_blank">expected to rise to nearly 4 billion tons by 2050</a>. Intuitively, trash collection seems like a job that hits all the Ds. Going through our worksheet, we confirm that globally, workers in this industry <a href="https://www.cdc.gov/niosh/docs/wp-solutions/2024-123/default.html" target="_blank">face</a> <a href="https://ilostat.ilo.org/beyond-the-bin-decent-work-deficits-in-the-waste-management-and-recycling-industry/" target="_blank">significant</a> <a href="https://data.bls.gov/cgi-bin/dbdown/ch" target="_blank">health hazards</a> (dangerous), and waste collection is <a href="https://occupational-prestige.github.io/opratings/opcrosswalk.html" target="_blank">ranked</a> as a <a href="https://link.springer.com/article/10.1007/s43615-021-00056-7" target="_blank">low-status job</a> (dirty), although interestingly, many workers <a href="https://www.annualreviews.org/content/journals/10.1146/annurev-orgpsych-031921-024847" target="_blank">take pride</a> in <a href="https://www.annualreviews.org/content/journals/10.1146/annurev-orgpsych-012420-091423" target="_blank">providing this essential service</a>.The job is also repetitive, but there are aspects that make it not dull. Specifically, workers cite the <a href="https://academic.macmillan.com/academictrade/9780374534271/pickinguponthestreetsandbehindthetruckswiththesanitationworkersofnewyorkcity/" target="_blank">day-to-day interaction with their coworkers</a> (which includes extensive insider vocabulary, work hacks, and mutual aid groups) and <a href="https://www.routledge.com/Collecting-Garbage-Dirty-Work-Clean-Jobs-Proud-People/Perry/p/book/9780765804105" target="_blank">task variety</a> 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) <a href="https://www.cdc.gov/niosh/docs/wp-solutions/2024-123/default.html" target="_blank">recommends the adoption of automated side loader trucks and collision avoidance systems</a>. 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 <a href="https://dl.acm.org/doi/10.1145/1349822.1349827" target="_blank">DDD is only one of many possible approaches</a> 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 (e.g., 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 <a href="https://spectrum.ieee.org/marco-hutter-ai-institute" target="_blank">RAI</a>, 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.<div class="horizontal-rule"></div><a href="https://rai-inst.com/wp-content/uploads/2026/02/Dull-Dirty-Dangerous.pdf" target="_blank">Dull, Dirty, Dangerous: Understanding the Past, Present, and Future of a Key Motivation for Robotics</a>, 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. Reference: https://ift.tt/8AF0qpi

Agentic AI for Robot Teams

<img src="https://spectrum.ieee.org/media-library/johns-hopkins-whiting-school-of-engineering-logo-with-shield-emblem.png?id=66700256&width=980"/> This presentation highlights recent efforts at the Johns Hopkins Applied Physics Laboratory to advance agentic AI for collaborative robotic teams. It begins by framing the core challenges of enabling autonomy, coordination, and adaptability across heterogeneous systems, then introduces a scalable architecture designed to support agentic behaviors in multi-robot environments. The talk concludes with key challenges encountered and practical lessons learned from ongoing research and development.Key learnings<ul><li>Provides an introduction to LLM-based AI Agents</li><li>Describes an approach to applying LLM-based AI Agents to robotic teams</li><li>Provides demonstrations of the approach running in hardware with a heterogeneous team of robots</li><li>Presents lessons learned and future work in this area</li></ul><div><a href="https://events.bizzabo.com/867156" target="_blank">Download this free whitepaper now!</a></div> Reference: https://events.bizzabo.com/867156