Apple’s Worldwide Developer’s Conference is set for June 8, and the company is known for making major announcements at the event. Apple will likely announce iOS 27, but will we finally see the fabled foldable iPhone? Will Siri get an AI upgrade? Will Apple bring AI to your Camera app? 

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The slow trickle of iOS 27 leaks continues with seemingly obvious upgrades that are being painted as part of a desperate move to catch up with competitors. Instead, they appear to be business as usual.

Apple announces new features for iOS each year during WWDC, and sometimes, early builds lead to leaks of features being tested. While these leaks frustrate Apple engineers that would prefer to keep them a surprise, they’re sometimes obvious and predictable.

According to a new leak from Bloomberg, Apple will announce improved Writing Tools, a wallpaper generator, and the ability to generate Shortcuts with natural language input. While these are not groundbreaking features, they’re part of a larger effort to ensure users have access to various AI tools across their iPhone.

The report goes out of its way to suggest that Apple is “racing to catch up with hardware rivals” while referencing Google’s Android Show filled with pre-announced features. Several of these, like importing grocery lists to apps from a first-party tool like Reminders, already exist on Apple platforms without the need for AI.

Ignoring the unnecessary color of the report, the new features are predictable, if useful, additions to iOS. They also hold up to my repeated assertion that Apple’s AI efforts will remain out of the way for those that don’t want to use them.

Writing Tools were introduced during WWDC 2024 as a part of Apple’s initial AI push. They’re able to generate or summarize text, but I’ve taken to using the Proofread function instead of paying for Grammarly.

Writing Tools could get more proactive in iOS 27

That feature is set to get an upgrade in iOS 27 that will bring Grammarly-style checks to the tool. Writing Tools already checks for spelling errors, punctuation mistakes, and similar basic changes, but it doesn’t look for syntax errors.

It doesn’t appear that Writing Tools will operate differently when invoked by the user, but one poorly explained part does stick out.

The report mentions that Writing Tools will appear from a translucent menu at the bottom, provide users with the ability to apply or ignore changes, and see edited text alongside revised text. That’s already in place today.

What’s new appears to be a briefly mentioned toggle that “pauses grammar checking.” That sounds more like the feature can be set to run automatically within a text field rather than relying on activating a specific tool.

Today, Writing Tools are found via a right-click menu or in the text suggestions box above the keyboard. Apple may bring more of the Writing Tools controls to the top of the keyboard.

Again, none of this is particularly revolutionary. In fact, they sound right out of my own wishlist for the Writing Tools feature.

More obvious updates

Apple previously introduced a whole new system for customizing the iPhone Lock Screen and Home Screen. It involves a wallpaper picker that pulls from Photos, preset wallpapers, or custom ones based on the weather or selected emoji.

A new AI generation tool for wallpapers could appear

It is only natural that Apple bring Image Playground into the mix. It’s already being used for generating images for Apple Invites, for example.

Then there’s the returning rumor about natural language processing for generating Shortcuts. This is an often repeated rumor that never came to fruition.

It seems iOS 27 will finally include the feature. Users will apparently be able to speak Shortcuts into existence rather than having to build them manually.

Apple’s more proactive Siri was also meant to suggest Shortcuts based on common and repeated tasks. Today, Apple Shortcuts is a useful tool, but only if you know how to build Shortcuts or get them from elsewhere.

Negative framing

I suppose it is fair to say that Google and Samsung have announced AI features, and even released some, for their smartphones. By that basic reality alone, it is easy to say that Apple is “behind” in this AI race, even if those features make the Android experience arguably worse.

However, I have never encountered a real human being, on the internet or otherwise, who has said these Android AI features were useful. Sure, they love their AI tools found in apps, but the on-device tools touted in these keynotes get crickets as far as I can tell.

It probably doesn’t help that the requirements for those features are incredibly specific. The fragmented nature of Android prevents feature parity across devices.

When I think of desperation, I imagine a company putting AI in a cursor so that everything becomes an AI interaction. But of course, no one would actually do that. Right?

Googlebooks will have an AI cursor

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Apple has seen record sales and demand in spite of it not drowning its products in unnecessary AI features. In fact, I believe part of the success can be attributed to the exact opposite.

The idea that Apple is somehow flailing internally is silly to me. Of course, the company would have preferred not having a two-year delay on some AI features, but that’s a different story.

Apple’s products are primed to be the best home for AI models, if you choose to use them. Not only will Apple ship its improved Apple Foundation Models and APIs for endpoint agents, it will give users the ability to do business as usual.

The only ones I see “racing to catch up” are Apple’s competitors who are hoping for some time in the spotlight before the inevitable AI collapse. At that point, Apple will have its iPhone while these AI-first companies will have to pivot to some other grift.

Most members of Stanford’s class of 2026 are smart, ambitious, and poised for remarkable careers. Theo Baker already has one. In his first semester of college, Baker broke the story that forced Stanford president Marc Tessier-Lavigne to resign — work that earned him a George Polk Award, one of journalism’s highest honors. Warner Brothers and producer Amy Pascal have optioned the rights to that story. And Tuesday, with graduation less than a month away, Baker publishes How to Rule the World, a sweeping account of his time at Stanford and the school’s often insidious relationship with the venture capital industry. Judging by early interest, it has every chance of becoming a bestseller.

We’ve been anticipating this one (we shared some related thoughts about it just a few weeks ago). We talked with Baker last Friday. This interview has been edited for length and clarity.

You showed up at Stanford as a coder. How did you end up breaking one of the biggest stories in the university’s history before your freshman year was even over?

I arrived thinking tech and entrepreneurship was the path for me. I joined the student hackathon, Tree Hacks, helped run it, skipped ahead to the CS weeder class. But my grandfather, with whom I was very close, had passed away a few weeks before I arrived, and he talked about working on the student paper more than anyone I’d ever known. So I joined the student paper to feel connected to him — it was supposed to be a hobby, a way to meet people and explore campus.

Very quickly things spiraled from there. My first few stories got more reception than we’d imagined, tips started flooding in, and one led me to a pseudonymous website called PubPeer, where scientists dissect published research. There were comments, seven years old at the time, suspecting that papers co-authored by Stanford’s president, Marc Tessier-Lavigne, had images that were duplicated, spliced, or otherwise irregular. I was a month into my time at Stanford when that investigation began, and by the time I was back for sophomore year, the president had resigned.

Were you warned off the story?

Multiple times, before I’d even published my first article. People warned me that Tessier-Lavigne was a person of very high integrity with a sterling reputation — that I didn’t want to do this, that it was going to place me in a very uncomfortable position within the institution. Which, of course, was not wrong. Over the course of the next 10 months, as the story widened, the pushback grew steeper. Within 24 hours of my first story, the board of trustees announced their own investigation. I quickly learned that one of the board members overseeing it had an $18 million investment in Denali Therapeutics, the biotech company Tessier-Lavigne co-founded. And the statement announcing the investigation praised his “integrity and honor”— in an investigation that was theoretically looking into his scientific integrity. So the investigation itself became an object of reporting. Tessier-Lavigne never once directly responded to a request for comment during my freshman year. Eventually he began sending missives to all of the faculty — which included all of my professors — describing my reporting as “breathtakingly outrageous and replete with falsehoods.” And then I began hearing more from his lawyers.

The book is really about something broader, though — what you call the Stanford inside Stanford. What does that mean?

Very soon after I arrived, I realized there was this parallel reality — an inside world — where the kids identified early as the next trillion-dollar startup founders are plucked from the crowd and placed into a world of access and resources. Yacht parties, slush funds, everyone texting the same billionaires for advice on weekends. As Stanford has become more famous as the home of great startups, it has become, according to some people at the university, increasingly difficult to spot actual talent. So many people arrive thinking they can be the next billion-dollar dropout that there’s an entire system of hangers-on whose job is to separate what they call the “wantrepreneurs” — people doing it because it looks good — from the so-called builders who actually have potential. It’s a system designed to sniff out the teenagers you can make a buck off of as early as possible.

The title of the book, it turns out, isn’t just a metaphor.

No. It’s literally the name of a so-called secret class at Stanford, taught by a Silicon Valley CEO. It’s not really a class. It’s more like a Skull and Bones for the aspiring tech elite. People aren’t getting course credit, but there are lectures, discussions, guest speakers, held once a week in the winter quarter on campus. When I arrived, it was a status symbol even to know it existed — that made you “rule-adjacent,” as one person told me. What this guy Justin was trying to do — as the students in the class told me — was what everyone seems to be trying to do: get in and network with the teenagers who can be useful to you, young. Only he figured out how to cloak himself in this mystique and make these talented, promising kids come to him, because he was promising them how to rule the world. He promised that the most brilliant students at Stanford would congregate in this 12-person seminar, and that the only way to learn these secrets was to go through him. It’s a very poignant example of how this system of talent extraction has come to manifest itself in strange ways.

What does that talent-scouting system actually look like on the ground?

There are VCs who employ older Stanford upperclassmen to identify freshmen as soon as they arrive on campus. It’s kept purposefully obscure. I’ve had people tell me it’s seen as an anti-signal to join one of the big entrepreneurship clubs, because that looks like you’re doing it for the title — as opposed to being in one of the secret feeder groups where the true builders supposedly congregate. But as much as there is genuine talent among the kids in this world, the primary qualification is who you know — whether you’re getting tapped on the shoulder. There was a CEO who cold-emailed me freshman year, asked to get to know me. The first time we went to dinner, we went to the Rosewood Hotel, and he’s sitting there spoon-feeding his eight-month-old caviar as he casually mentions that his first-ever contract was for Muammar Gaddafi. That casualness is something I find fascinating. And this whole system goes a long way toward explaining how the big frauds develop. It starts by vesting huge amounts of authority, money, and power in the hands of teenagers without adequate safeguards for when things go wrong.

You arrived right as the FTX collapse was happening and ChatGPT launched. What was that like to observe up close?

The timing was almost absurd. We arrived at the tail end of the crypto craze — the assumption when we showed up was that crypto was how you were going to make your fortune. SBF begins his descent on November 2nd. ChatGPT comes out November 30th. And immediately everything pivots. I remember being at a dinner shortly after ChatGPT’s release, sitting with one of the biggest crypto boosters on campus, and he’s telling me that SBF was “directionally correct” — that was the phrase — but that everyone was trying to figure out how to get around the legality. And quickly, many of those same people realized that AI was the new craze they could jump on. They told me they could reach the same heights as SBF, preferably without the fall, by taking advantage of the newest new thing. Silicon Valley operates in cycles, but this one has been particularly fascinating to observe up close because the scale is just unfathomable.

Do you think your peers are leaning into entrepreneurship partly out of anxiety about the job market?

Absolutely. The AI rush has made talent the resource to mine in this modern-day gold rush — the most valuable researchers and founders are more valuable than ever, but entry-level positions are starting to disappear. There’s a common refrain among people in this world that it’s easier to raise money for a startup right now than to get an internship. Which is remarkable, right? Entrepreneurship, rather than being the non-conformist outsider thing it might once have been associated with, has become an expected path. That changes the nature of it entirely.

What’s one piece of advice you’d give to a 17-year-old heading to Stanford or any elite university today?

You have to be really conscious about whether you’re doing what you’re doing because you believe in it and because it’s the right thing — or because it’s the easy thing. It’s very easy to be buffeted by trends and the tech whirlpool, to find yourself wasting away at a job you don’t actually want because you followed the expected path. Following the expected path is way less interesting than going out and doing something for yourself. I admire the best founders who emerge from this place because they feel genuinely empowered to make a difference. You just have to be careful that you’re doing it for the right reasons — and not just because you want to get rich.

You came here thinking you’d be a founder. Do you still want to start something?

Honestly, I haven’t thought about it that much — it’s been a mad dash to finish the book and get to graduation, which is astonishingly only about a month away. But I think it comes across in the book that I really did fall in love with journalism. It’s a temperament, almost an affliction, more than a career. Whatever I do, it will intersect with that.

Redmi is preparing to launch its Turbo smartphone lineup in India, starting with the upcoming Redmi Turbo 5. This phone was initially released in China a few months ago and will soon be available in India as well. The Redmi Turbo 5 will reportedly emphasize gaming, battery life, and fast charging. Xiaomi has confirmed that the smartphone will be available through Amazon India. The microsite also gives users a first look at the Indian version of the smartphone through teaser images. However, Xiaomi is still keeping the official launch date under wraps for now.

Redmi Turbo 5 Design Revealed

The teaser images shared by Xiaomi offer a clear look at the Redmi Turbo 5’s design ahead of its launch. The smartphone appears similar to the version that launched earlier in China. It includes two rear camera lenses positioned on the top-left side, with an LED flash next to them. Xiaomi has also confirmed a black color variant for the Indian market. The phone carries Redmi branding on the flat rear panel, and the right side houses both the power button and volume controls.

Expected Specifications of Redmi Turbo 5

In terms of specifications, the Redmi Turbo 5 will most likely prioritize performance and battery life. The device may come with a 6.59-inch 1.5K AMOLED screen that supports a 120Hz refresh rate, delivering outstanding performance when playing games or browsing. The phone may come powered by the MediaTek Dimensity 8500-Ultra chipset and offer 16 GB RAM along with 512 GB of internal storage. It may also have a Sony IMX882 primary camera sensor capable of capturing up to 50 MP images with OIS.

This sponsored article is brought to you by Melbourne Convention Bureau (MCB) supported by Business Events Australia.

Melbourne’s reputation as a global events city, from the Australian Open tennis and Formula 1 Australian Grand Prix to hosting NFL regular season games, now intersects with a different form of scale: large-scale compute, data-intensive research, and advanced engineering. Long recognized for delivering complex international events, the city is applying the same organisational capability to the infrastructure that underpins modern AI research, positioning Melbourne at the convergence of global convening and high-performance digital systems.

Consistently ranked among the world’s most livable cities, Melbourne was named Time Out’s Best City in the World in 2026, the first Australian city to hold the title.

More materially for research and innovation, Melbourne is also the nation’s fastest‑growing capital, attracting increasing concentrations of engineering and technology talent, investment and international engagement.

Australia’s artificial intelligence (AI) ecosystem is entering a new phase, defined less by isolated initiatives and more by the convergence of compute infrastructure, research intensity and international collaboration. Melbourne sits at this intersection.

Melbourne’s trajectory highlights what enables research at scale: access to frontier-grade compute, proximity to industry-ready infrastructure, and repeated opportunities for global research communities to convene.

Sovereign AI compute, expanding hyperscale data center campuses and a growing pipeline of international research-led conferences are reshaping the city’s research landscape. Together, these elements position Melbourne as a focal point for applied AI research, advanced engineering and data-intensive science.

The growing global influence of AI engineering, underscored by NVIDIA CEO Jensen Huang receiving the 2026 IEEE Medal of Honor, reflects the scale of this shift. In Melbourne, these factors form a reinforcing research flywheel linking infrastructure, discovery and collaboration.

Rather than focusing on startup density or short-term commercial output, Melbourne’s trajectory highlights what enables research at scale: access to frontier-grade compute, proximity to industry-ready infrastructure, and repeated opportunities for global research communities to convene.

NVIDIA CEO Jensen Huang received the 2026 IEEE Medal of Honor.IEEE

Sovereign AI foundations

The most recent cornerstone of Melbourne’s AI capability is MAVERIC (Monash AdVanced Environment for Research and Intelligent Computing), Australia’s largest university-based AI supercomputer. Built and deployed by Monash University in partnership with NVIDIA, Dell Technologies, and CDC Data Centres, MAVERIC has been engineered specifically for large scale AI and data intensive science, with medical research representing a key priority. Indeed, in these regards MAVERIC has been designed to function as a Next Generation Trusted Research Environment thus ensuring that it is state-of-the-art and provides a safe and secure framework for the analysis of large sensitive datasets.

MAVERIC has been designed to function as a Next Generation Trusted Research Environment thus ensuring that it is state-of-the-art and provides a safe and secure framework for the analysis of large sensitive datasets.Monash University

Designed to support research projects including cancer and neurodegenerative disease detection, clinical trial analysis and drug discovery through to materials science and engineering, MAVERIC enables Australian researchers to train and evaluate large models domestically while keeping highly sensitive datasets secure and under national jurisdiction. This sovereign design is particularly relevant in fields such as medical research where privacy, regulation or intellectual property constraints limit the use of offshore cloud resources.

Monash University Vice-Chancellor and President Professor Sharon Pickering with researchers [left to right] Professor Anton Peleg, Professor Victoria Mar, Professor James Whisstock, Vice-President (Strategy and Major Projects) Teresa Finlayson, and Professor Patrick Kwan.Eamon Gallagher (Australian Financial Review)

Technically, the system reflects the latest shifts in high performance AI architecture. Built on NVIDIA GB200 NVL72 platforms and integrated using Dell’s rack scale infrastructure, MAVERIC employs closed loop liquid cooling to reduce water consumption compared with conventional air-cooled systems, aligning large scale compute growth with sustainability objectives while supporting high density, high throughput workloads.

Professor James Whisstock, Deputy Dean Research of Monash’s Faculty of Medicine Nursing and Health Sciences commented, “MAVERIC provides a huge leap forward in our compute capability that will revolutionize our researchers’ ability to address the most challenging and important research questions across the fields of medical research, information technology, and STEM disciplines. It will seed wonderful new cross-disciplinary collaborations, underpin the work of our best and brightest young researchers and will allow our scientists to continue to make major discoveries that positively impact the Australian and global population more broadly.”

“MAVERIC provides a huge leap forward in our compute capability that will revolutionize our researchers’ ability to address the most challenging and important research questions across the fields of medical research, information technology, and STEM disciplines.” —James Whisstock, Monash University

Monash University frames MAVERIC not as a standalone asset, but as part of the national research infrastructure, intended to strengthen collaboration across academia, healthcare, government and industry. This approach positions Melbourne at the forefront of sovereign AI enabled research in the region.

Data centre scale as research infrastructure

The infrastructure demands of modern AI research extend well beyond individual systems. Melbourne’s expanding data centre footprint now supports hyperscale compute, applied AI deployment and large-scale research workloads simultaneously.

Total data center investment, US$ billions.Source: Data Centres Global Report 2025

In February 2026, CDC Data Centres opened its first Melbourne campus in Brooklyn, with two live facilities and a third in planning. Combined with CDC’s Laverton campus, Melbourne is projected to host more than 800 megawatts of sovereign digital capacity, critical for AI workloads requiring sustained access to high-density power, cooling and secure environments.

Parallel investment is underway in Fishermans Bend, where NEXTDC is developing a AUD $2 billion AI and digital infrastructure hub adjacent to the Innovation Precinct. Planned facilities include an AI Factory, a Mission Critical Operations Centre and a Technology Centre of Excellence, enabling sovereign AI, high-performance computing and cross-sector collaboration across health, defence and finance.

Melbourne hosts Australia’s largest cluster of AI firms, with 188 companies, and more than 40 data centres currently operate across Victoria. The Victorian Government has complemented this growth with an initial AUD $5.5 million investment in the Sustainable Data Centre Action Plan.

Together, these developments reinforce Melbourne’s role as a national and increasingly global hub for high-performance AI infrastructure as model complexity and infrastructure dependency continue to accelerate.

Applied AI research at scale

Monash University is home to MAVERIC, Australia’s largest university-based AI supercomputer, built and deployed by Monash in partnership with NVIDIA, Dell Technologies, and CDC Data Centres.Monash University

Melbourne’s research strength is underpinned by a dense university network with deep capability across AI, data science and engineering. Institutions including Monash University, the University of Melbourne, Deakin University, La Trobe University, RMIT University and Swinburne University of Technology collectively support research across machine learning, robotics, human-computer interaction, extended reality and advanced manufacturing.

This concentration fosters applied collaboration where AI intersects with medicine, sustainability, cognitive systems and immersive technologies. For visiting researchers, it provides access not only to academic expertise but also to live infrastructure environments where research can be tested and validated, reinforcing Melbourne’s position as one of the Asia-Pacific’s most integrated AI research ecosystems.

Conferences as research accelerators

Plenary session at Melbourne Convention and Exhibition Centre.Melbourne Convention Bureau

Melbourne’s selection as host city for a growing number of international technology conferences reflects the convergence of research capability and infrastructure maturity.

In September 2026, Data Center World Australia and The AI Summit Australia will be co-located at the Melbourne Convention and Exhibition Centre, bringing together global leaders across AI, digital infrastructure and enterprise technology. The pairing highlights a broader reality: advances in AI are inseparable from the infrastructure that enables them.

Melbourne’s expanding data centre footprint now supports hyperscale compute, applied AI deployment and large-scale research workloads simultaneously.

Research-led conferences are also expanding Melbourne’s global footprint. ICONIP 2026, hosted by Deakin University, will bring up to 700 researchers in neural networks and machine learning, followed in 2027 by IEEE VR, the leading conference on virtual reality and 3D user interfaces, attracting up to 1,000 delegates.

In this context, conferences function not simply as events, but as infrastructure for knowledge transfer, supporting standards exchange, collaboration and system-level learning at global scale.

A global platform for advancing research

Sovereign compute, data centre scale and a strong conference pipeline create a reinforcing cycle, enabling researchers to engage directly with infrastructure and industry well beyond the event itself.

By closing the gap between theory and deployment, Melbourne supports deeper technical exchange and more enduring global research networks.

This role was recognized in 2025 when the IEEE awarded Melbourne Convention Bureau the 2025 Organisational Supporting Friend of IEEE Member and Geographic Activities (MGA) — the first convention bureau in the Asia Pacific region to receive the acknowledgement as a result of the longstanding partnership with the IEEE Victorian Section.

Melbourne Convention Bureau (MCB) representative Fatima Aboudrar, Senior Business Development Manager, with Vijay S. Paul, Immediate Past Chair, IEEE Victorian Section, receiving Supporting Friend Member recognition in 2025.

As AI research becomes increasingly dependent on infrastructure scale, sovereign capability, and global collaboration, Melbourne is moving beyond hosting conversations to actively enabling the systems that advance AI and data‑driven research at global scale.

Conference support in Melbourne

Melbourne Convention Bureau

This ecosystem is underpinned by Melbourne’s highly accessible city centre, where world-class venues, research institutions and industry hubs are located in close proximity. Free public transport and a compact city footprint enable seamless movement from conference floor to real-world application.

Melbourne Convention Bureau (MCB) supports professionals in bidding for, securing and delivering international conferences across Melbourne and regional Victoria. Backed by the Victorian Government, MCB has for more than 60 years helped bring the world’s leading thinkers to the state, positioning Melbourne as a place where ideas become impact.

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.

New York public health provider NYC Health + Hospitals says a months-long data breach that allowed hackers to steal personal data, medical records, and fingerprints scans affects at least 1.8 million people.

NYCHHC is the largest public health system in the United States and provides healthcare to over a million New Yorkers, the majority of whom are uninsured or receive state healthcare benefits, such as Medicaid.

The healthcare system reported the number to the U.S. Department of Health and Human Services, making it one of the largest healthcare-related data breaches of the year so far. Healthcare organizations have been repeatedly targeted by financially motivated cybercriminals in recent years in efforts to steal their vast banks of highly sensitive patients’ personal, medical, and billing information.

In a data breach notice on its website, NYCHHC said that it detected a cyberattack on February 2 and secured its network. The hackers had access to its network from November 2025 until February 2026, during which the hackers copied files from its systems.

The healthcare system said hackers broke due to a breach at a third-party vendor, which it did not name.

NYCHHC said that the exposed data varies by individual and includes patients’ health insurance plan and policy information, medical information (e.g., diagnoses, medications, tests, and imagery), billing, claims, and payment information. Other government-issued identity documents, such as Social Security numbers, passports, and driver’s licenses, were also compromised.

The breach notice also says “precise geolocation data” was taken in the breach, suggesting that the user-uploaded photos of their identity documents may have also contained the exact location of where the document was captured.

The breach is particularly sensitive because hackers stole biometric information, including fingerprints and palm prints, which affected individuals have for life and cannot replace. NYCHHC did not provide an explanation for storing biometric data. Prospective NYCHHC employees are generally required to enroll their fingerprints for criminal records checks. It’s not yet known if patients’ biometrics were also taken.

NYCHHC’s website was briefly offline as of Monday morning. A spokesperson for NYCHHC did not immediately respond to an email from TechCrunch with questions about the cyberattack. TechCrunch asked, among other things, why it took the organization months to detect the breach, and if it has received any communication from the hackers, such as a demand for payment.

It’s not clear if NYCHHC can receive email at the time of the website outage.

The incident appears to be unrelated to the data breach at National Association on Drug Abuse Problems (NADAP) earlier this year, in which over 5,000 NYCHHC patients had information taken in the cyberattack.

In the FBI’s latest annual report on cybercrime covering 2025, healthcare remained a top target for ransomware attackers — criminals who break into databases, steal a copy of the data while scrambling the victim’s servers, and threaten to publish the stolen data if the victim does not pay the hackers. A ransomware attack on UnitedHealth-owned health tech giant Change Healthcare allowed Russian-linked hackers to steal the medical and billing information of more than 190 million Americans, believed to be the largest theft of U.S. medical data in history.

Elon Musk’s claim that he was mistreated by his OpenAI co-founders failed after nine California jurors returned a unanimous verdict that his lawsuits had been filed too late.

Musk accused Sam Altman, Greg Brockman, OpenAI, and Microsoft of “stealing a charity” by creating a for-profit affiliate of the frontier AI lab. Jurors, however, found that any harms that Musk may have suffered came before the deadline for filing his claims under the law.

While the trial delved deeply into the melodramatic history of OpenAI and featured testimony from leading figures in Silicon Valley, it ultimately turned on fairly narrow questions of the law. The trial focused on whether and when Altman and the other defendants had made and broken promises to Musk, but his case failed to convince jurors that he had a valid claim.

In particular, OpenAI had advanced a statute of limitations defense, which sought to prove that any harms Musk sought to litigate had taken place before 2021. (The specific date varied by the charge: before August 5, 2021, for the first count; August 5, 2022, for the second count; and November 14, 2021, for the third count.) Ultimately, the jury found that argument persuasive, which made for a short deliberation period.

“There was a substantial amount of evidence to support the jury’s finding, which is why I was prepared to dismiss on the spot,” Judge Yvonne Gonzalez Rogers said after the verdict was delivered.

The end of the case means that one major threat to OpenAI — a possible restructuring — is now off the table ahead of its reported IPO.

“It did not take [the jury] two hours to conclude … that Mr. Musk’s lawsuit is nothing more than an after-the-fact contrivance that bears no relationship to reality,” OpenAI’s lead attorney, Bill Savitt, said after the verdict. “They kicked it exactly where it belongs — just to the side. This lawsuit is a hypocritical attempt to sabotage a competitor.”

Microsoft, which Musk sued for aiding and abetting OpenAI’s alleged breach of charitable trust, welcomed the verdict. A spokesperson for the company said it “remained committed to our work with OpenAI to advance and scale AI for people and organizations around the world.”

The verdict came in the middle of a hearing to determine the potential damages to Musk if the verdict had gone the other way. While that discussion is moot for now, the judge appeared unconvinced by the analogy Musk’s lawyers drew between his charitable contributions and investments in a for-profit startup.

“Your analysis seems to be devoid of connection to the underlying facts,” she told Dr. C. Paul Wazzan, the expert who came up with Musk’s estimate of OpenAI and Microsoft’s wrongful gains at his expense — some $78.8 billion to $135 billion.

In a tweet after the ruling, Musk appeared to take the procedural grounds of the dismissal as a moral victory. “There is no question to anyone following the case in detail that Altman & Brockman did in fact enrich themselves by stealing a charity. The only question is WHEN they did it!” Musk wrote. “I will be filing an appeal with the Ninth Circuit, because creating a precedent to loot charities is incredibly destructive to charitable giving in America.”

Reached for comment by TechCrunch, Musk’s lead counsel, Marc Toberoff, said, “One word: Appeal.”

Stress on organisations to patch vulnerabilities a ‘major concern’ for NCSC’s Joseph Stephens.

The European Commission and several EU member states, including Ireland, are in talks with Anthropic over Mythos, as countries worldwide scramble to protect their vulnerable critical infrastructure from cyber risks.

“We’ve had direct contact with them,” the National Cyber Security Centre (NCSC)’s director of resilience Joseph Stephens told SiliconRepublic.com, referring to Anthropic, whose European headquarters is based in Ireland.

“But we’re working through the European system because there’s more strength in having a coordinated approach.” Stephens was speaking to SiliconRepublic.com during the recent ZeroDayCon event in Dublin.

Mythos sent shockwaves through the industry following its limited launch to select big businesses more than a month ago. Anthropic’s move received much praise from experts, including from Stephens, who called on other frontier AI model providers to do the same.

Is control out of bounds?

Mythos’s development, Anthropic claims, was not intentional, but merely the result of a “downstream consequence of general improvements in code, reasoning and autonomy”.

And undoubtedly, states don’t wield much power when it comes to controlling technological advancements or managing how it is disseminated – at least initially.

The growing concern around AI-induced cybersecurity risks is by no means restricted to Mythos. Put together, these factors create a difficult environment for legislators as they attempt to catch up to new innovations in this space cropping up faster than ever before.

“We have to recognise what the Irish state can and cannot do,” Stephens said. “We can’t stop a company like Anthropic based in the US from releasing or not releasing a model.”

Mythos’s launch created such a ripple that the NCSC – for the first ever time – released a statement on a specific product release.

Meanwhile, nations worldwide, including the US, UK, Canada and Japan, were quick to invite Anthropic to discussions to potentially employ the model to bolster their critical infrastructure security.

However, governments and big businesses aside, start-ups and SMEs with limited resources to bolster their cybersecurity are particularly at risk.

“The major concern here right now is the stress that [Mythos] may place on organisations who are now going to have to patch all of their digital products and services,” Stephens said.

But ironic as it is, AI might come in handy for situations such as these.

Will the EU AI Act help?

Stephens called for a joint effort between states to come up with a common regulatory approach around such models.

“The AI Act allows us to ensure that products that come onto our marketplace are done in a secure and a safe way,” he said. “Europe has really pushed forward with the AI Act … [but] we can’t regulate our way out of it.”

The line between regulating and stifling innovation is one that the EU is arguably still in search of.

It is attempting to remedy some of this over-regulation by way of a simplified and consolidated set of rules. Earlier this month, the bloc adopted new provisional rules on the AI Act.

The AI Act applies to businesses that sell into the EU, or if the AI output is used in the EU. The landmark regulation attempts to balance managing the risks of this technology while letting the EU benefit from its potential.

According to law expert Dr TJ McIntyre, it is possible to regulate models such as Mythos with extraterritorial effect, but only if they are sold into the EU or if their outputs are sold into the region.

McIntyre is an associate professor in the Sutherland School of Law at University College Dublin.

“It’s not clear that the AI Act would apply if Mythos is geo-restricted for use outside the EU,” he explained.

However, the Act is “designed to address ‘offensive cyber capabilities, such as the ways in which vulnerability discovery, exploitation or operational use can be enabled’ as a type of systemic risk,” he said – so, “in theory”, the EU could take action under the AI Act.

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