2026-05-20 19:00:01
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 $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.
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.
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.
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.
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.
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.
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.
2026-05-19 02:00:01
In the late 1940s—when computer engineers were grappling with unreliable hardware and noisy transmission environments—a team of engineers inside a modest lab at the University of Manchester, England, confronted a problem so fundamental that it threatened the viability of digital computing itself. Machines could generate bits, but they could not reliably read them back.
The inconsistent reading back of memory data did not initially present itself as a grand theoretical challenge. It showed up as something more mundane: inconsistent computing results.
Engineers including Frederic C. Williams, Tom Kilburn, and G. E. (Tommy) Thomas traced the failures not to logic errors but to the physical behavior of the machines themselves. The team devised a technique for keeping a transmitter and a receiver synchronized without relying on a separate clock signal. Their innovation, known as Manchester code or phase encoding, encoded each bit with a transition in the middle of the bit period, effectively embedding timing information directly into the data stream to be a self-clocking signal. So, even if the signal degraded or the timing drifted slightly, the receiver could continually keep time based on those regular transitions.
By eliminating the need for separate clocks and reducing synchronization errors, Manchester code made data transfer more robust across cables and circuits.
Those qualities later made it a natural fit for technologies such as Ethernet and early data storage systems. Its self-clocking nature helped standardize how machines communicate, and it laid the groundwork for modern networking and digital communication protocols.
On 13 April 2026, this breakthrough was honored with an IEEE Milestone plaque during a ceremony at the University of Manchester. Dignitaries from IEEE and the university attended the ceremony.
Those 1940s Manchester University engineers were working on systems that fed into the Manchester Mark I, one of the first practical stored-program machines.
When troubles arose, they used oscilloscopes to probe signals. They found that electrical pulses did not arrive with consistent timing. Memory signals also blurred over time, making them harder to read, and when long runs of identical bits occurred, the waveform flattened into stretches with no transitions.
That led to a crucial insight: The problem was not just detecting whether a signal was high or low; the system also lost track of when to sample the signal. Without reliable timing markers, even correctly formed signals were misread. Bits could effectively be lost or miscounted because the system fell out of sync.
At first, the engineers tried to tame the hardware. They experimented with stabilizing circuits and more consistent pulse generation, attempting to impose a regular rhythm on an inherently unstable system. But the fixes proved fragile, and the electronics of the day could not maintain the required precision. So the Manchester group took a different approach.
If the hardware could not provide a dependable clock, the signal itself would have to carry one. Instead of representing data as static levels, each bit changed state, with a guaranteed transition in the middle.
Embedding timing in the signal reduced erratic behavior. Machines were suddenly able to reliably transmit, store, and read back data—an essential step toward practical stored-program computing.
The Manchester code addressed several issues at once. Regular transitions allowed continuous timing recovery. Transitions proved easier to detect than static levels, and long runs of identical bits no longer produced flat, ambiguous waveforms. Rather than fighting the imperfections of early electronics, the design worked with them.
What began as a local solution in Manchester shaped digital communication systems for decades, including early Ethernet technology, for which timing and shared-medium communication were central challenges.
According to Robert Metcalfe, a member of the team that built the first Ethernet system at Xerox PARC in 1973, he and his colleagues relied on Manchester code.
“Manchester code solved a fundamental problem for us: timing,” Metcalfe says, explaining that each bit carried its own clock and removed the need for a global synchronized signal.
That self-clocking property wasn’t the only benefit provided by the encoding scheme. On a shared coaxial cable, Manchester encoding did more than provide timing. Each transceiver left the medium undriven—effectively “off”—most of the time, allowing packets from other machines to pass without interference. Even during transmission, a station drove the signal only about half the time, leaving the line undriven during the other half of each bit cycle.
This distinction—between a driven signal and an undriven line, rather than simple 1s and 0s—allowed receivers to recover both data and clock timing while also monitoring the cable for other activity. If a transceiver detected a signal when it expected the line to be undriven, the signal indicated that another station was transmitting at the same time. In other words, the system could detect collisions in real time and respond accordingly.
The idea has proven durable far beyond local networks. Manchester code is being used aboard the Voyager spacecraft, which are now cruising through interstellar space—underscoring its reliability in extreme environments.
The code also has found its way into everyday consumer electronics. Infrared remote controls for televisions and audio equipment commonly rely on Manchester code through protocols such as RC-5, developed by Philips in the early 1980s. The protocol encodes commands as timed infrared signals transmitted by a handset’s integrated circuit and LED, allowing devices to reliably interpret button presses even through noise and signal distortion. Manufacturers across Europe—and many in the United States—adopted the approach, extending Manchester code into the home.
An IEEE Milestone designation recognizes technologies with enduring impact. Manchester code qualifies because it solved a foundational timing problem at a critical moment in computing history.
Without a way to embed timing in the data itself, early digital systems would have remained fragile and unreliable. Manchester code helped transform them into dependable machines, and it enabled much of today’s digital communication.
“Manchester code solved a fundamental problem for us: timing,” —Robert Metcalfe, an Ethernet inventor
Key participants at the plaque dedication ceremony included Tom Coughlin, 2024 IEEE president; Duncan Ivison, University of Manchester president and vice chancellor, and Nagham Saeed, chair of the IEEE U.K. and Ireland Section.
Talks by Kees Schouhamer Immink (the 2017 IEEE Medal of Honor laureate probably best known for his work that made compact discs and other high-density digital media practical) and Peter Green (Manchester’s deputy dean for the engineering faculty) highlighted the code’s lasting impact on digital data storage and communications.
The IEEE Milestone plaque for the Manchester code reads:
“At this site in 1948–1949, Manchester code was invented for reliably encoding digital data stored on the Manchester Mark I computer’s magnetic drum. It became a standard for computer magnetic tapes and floppy disks and was used in digital communications, including the Voyager 1 and 2 spacecraft and early Ethernet networks. It found wide use in domestic remote controllers, radio frequency identification (RFID) tags, and many control network standards.”
Administered by the IEEE History Center and supported by donors, the Milestone program recognizes outstanding technical developments worldwide. The IEEE U.K. and Ireland Section sponsored the nomination.2026-05-18 21:00:00
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.
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.
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.
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.
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.2026-05-18 18:00:01
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.
Melbourne, Australia’s premier conference destination. Tourism Australia
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
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.
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.” —Professor James Whisstock, Deputy Dean Research of Monash’s Faculty of Medicine, Nursing, and Health Sciences
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.
The infrastructure demands of modern AI research extend well beyond individual systems. Melbourne’s expanding data center 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 Center and a Technology Center 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 centers currently operate across Victoria. The Victorian Government has complemented this growth with an initial AUD $5.5 million investment in the Sustainable Data Center 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.
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.
Plenary session at Melbourne Convention and Exhibition Center.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 Center, 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 center 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.
Sovereign compute, data center 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.
Why host a conference in Melbourne, Australia.Melbourne Convention Bureau
This ecosystem is underpinned by Melbourne’s highly accessible city center, 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) is a not-for-profit state government agency with over 60 years’ experience, that provides IEEE and its members with free support to bring international conferences to Melbourne, Australia. MCB’s support spans early-stage exploration and international bidding through to securing government funding, connecting organizers with venues, accommodation and event suppliers, and providing destination support for conference planning and delivery. Organizations considering a conference in Australia are encouraged to connect with MCB’s dedicated team, which supports IEEE conferences in Melbourne. Enquiries can be directed to [email protected].
2026-05-18 18:00:01
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
2026-05-15 21:00:01
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Reprinted with permission from Trinity: An Illustrated History of the World’s First Atomic Test by Emily Seyl with contributions by Alan B. Carr, published by The University of Chicago Press. © 2026 by The University of Chicago. All rights reserved.
In the North 10,000 photography bunker, Berlyn Brixner was listening to the countdown on a loudspeaker, his head inside a turret loaded with cameras and film. He was one of the only people instructed to look toward the blast—through his welder’s glasses—ready to follow the path of the fireball as it launched into the sky. The two Mitchell movie cameras at his station would deliver the best footage to come of the Trinity test, used by Los Alamos scientists to make some of the first measurements of the effects of a nuclear explosion.
When the detonators fired, the cameras captured what Brixner could not have seen—the very first light of a violent, silent sea of energy unfurling into the basin. As 32 blocks of high explosives erupted all together, their incredible force surged inward toward the sleeping plutonium core, compressing the dense sphere of metal instantaneously from all sides and bringing its atoms impossibly close together. A carefully timed burst of neutrons sowed momentary, uncontrolled chaos, and then, as quickly as it began, the fission chain reaction ended. Footage from a high-speed Fastax camera in Brixner’s bunker, shot through a thick glass porthole, shows a translucent orb bursting through the darkness less than a hundredth of a second after detonation, as a rush of heat, light, and matter blew apart the Gadget.
When the brightness faded enough for witnesses to make out ground zero, they saw a wall of dust rise up around a brilliant, shape-shifting, multicolored ball of flames—forming a fiery cloud that shot into the sky atop a twisting stream of debris. The camera footage tells a story no less dramatic but hundreds of times more intricate, preserving the moment for scientists to return to again and again to measure and describe the behavior of the fireball and other visible effects with exacting detail. On balance, the photography effort was a huge success, despite only 11 of the 52 cameras producing satisfactory images. By arranging those cameras at intentionally staggered distances, complementary angles, and with a broad spectrum of frame rates and focal lengths, the Spectrographic and Photographic Measurements Group was able to piece together a remarkably complete picture of their subject.
On 12 July 1945, Herbert Lehr, a U.S. Army sergeant and electrical engineer assigned to Los Alamos, delivered the plutonium core to the McDonald ranch house, where the bomb was assembled. Los Alamos National Laboratory
According to the group’s leader, Julian Mack, the more than 100,000 frames that were captured still “give no idea of the brightness, or of time and space scales.” Mack attributed fortune, as much as foresight, to the photographic record that was made, especially during the earliest phase of the blast. Indeed, the explosion was several times more powerful than predicted, and the intensity of its effects overwhelmed many of the cameras and diagnostic instruments. The human observers were similarly overcome. “The shot was truly awe-inspiring,” said Norris Bradbury, the physicist who would succeed Robert Oppenheimer as director of Los Alamos. “Most experiences in life can be comprehended by prior experiences, but the atom bomb did not fit into any preconception possessed by anybody. The most startling feature was the intense light.”
Norris Bradbury, the physicist responsible for the final assembly of the Gadget, stands next to the partially assembled bomb at the top of the shot tower. The cables on the outside of the bomb would transmit the signals to trigger the synchronized detonations of conventional explosives, which would then create the inward-directed shock wave that would compress the bomb’s plutonium core. Bradbury would go on to succeed Robert Oppenheimer as director of Los Alamos on 17 October 1945.Los Alamos National Laboratory
It is a common sentiment that words and even pictures pale in comparison to the experience of the explosion. Even so, soldiers, scientists, and many other witnesses have added their firsthand accounts—often absorbing and poetic—to complement the trove of hard data collected during the test shot. They describe an intense and blinding brightness that filled the basin with daytime; an ominous, darkening cloud rearing its head in eerie silence; the wait for the invisible wave rushing out from the heart of the Gadget; and the mighty roar that arrived at last, in a thunder, and seemed never to leave. Physicist Isidor Isaac Rabi, watching from 20 miles away, remembered, “It blasted; it pounced; it bored its way right through you.”
James Chadwick, head of the British contingent of scientists who joined the Manhattan Project, later said, “Although I had lived through this moment in my imagination many times during the past few years and everything happened almost as I had pictured it, the reality was shattering.”
The blast, captured with an assortment of high-speed and motion-picture cameras, shows the fireball expanding between 25 milliseconds and 60 seconds, by which time the mushroom cloud is over 3 kilometers high.Los Alamos National Laboratory
And physicist George Kistiakowsky found himself certain that “at the end of the world—in the last millisecond of the Earth’s existence—the last human will see what we saw.”