2026-07-13 21:00:01
In 2005, Nokia sold its billionth mobile phone, a budget-friendly device that went to a customer in Nigeria. By then, the company, based in Espoo, Finland, was making one of every three cellphones globally.
But just nine years later, the mobile-device maker offloaded its entire handset division to Microsoft for pennies on the dollar, compared to what it had been worth at its peak.
Nokia had risen from obscurity in the 1990s to become a worldwide cultural phenomenon by the turn of the millennium, its signature devices featured in TV shows and movies, announcing their presence with instantly recognizable Nokia ringtones.
As Nokia was becoming comfortable in the spotlight, the smartphone era arrived. And what came next was swift and brutal. But, as revealed in Nokia internal documents recently made public and interviews with key Nokia engineers from that era, the company saw it coming. Within 24 hours of Apple CEO Steve Jobs’s iPhone unveiling in 2007, Nokia was already weighing its options. They’d immediately recognized the threat. However, outrunning it was another matter.
What follows is Nokia’s story over 14 years, from 1998 to 2012, as the world’s top cellphone maker—how its devices defined their time, how the tech reshaped what phones could be and do, and how the company’s good fortunes in the handset business came to an end.
The centerpiece Nokia devices, the ones that people probably think of when they see the words “Nokia phone,” were the 3210 and its cousin, the 3310. TechRadar has called the 3310 “the greatest phone of all time.”
Nokia’s 3210 phone, released in 1999, was an inexpensive device aimed at younger users. Colin McPherson/Alamy
Released in 1999 and 2000, respectively, the two devices sold more than 280 million units worldwide. Their most innovative hardware feature was the internal antenna—the first mass-market phone without even a stub or retractable aerial. “Consumers had the perception that it could not work well without an external antenna,” said Peter Røpke, a former Nokia senior vice president, in a 2016 interview with Slate.
The phones shipped with games, including the legendary Snake, one of the most popular pre-smartphone mobile games—in which a pixelated serpent eats and grows with every morsel consumed.
Nokia introduced no small portion of the world to texting. At the time of the 3210 and 3310, the prevailing texting standard was SMS (short message service), which allowed up to 160 characters per message. Nokia appended its own Nokia smart-messaging service to SMS, which allowed the sending of small bitmapped images across an otherwise text-only system. A rich-text messaging system that allowed visual images, audio, and video followed in 2002, leading to a multimedia messaging service (MMS) standard that remains in place today.
Nokia also enabled users to easily create and share ringtones on their devices. By 2000, Nokia’s custom-ringtone Composer app had popularized a new, short-form musical medium that the ringtone industry, at its peak, would transform into a billion-dollar marketplace in the United States.
Nokia introduced its 1100 phone in 2003 and ultimately sold half a billion units, making it the most popular cellphone in history. Paul Chesne/Donaldson Collection/Getty Images
A few years later, Nokia reimagined its mobile handsets, releasing the 1100 in 2003. The 1100 sold a half a billion units, more than any cellphone in history. It remains one of the best-selling consumer products ever. Much of the 1100’s success was due to its price tag—in the neighborhood of US $100, making it at the time Nokia’s most affordable device.
Also contributing to the 1100’s popularity were features designed for longevity and tough environments, including dust resistance, nonslip sides for better handling in rainy conditions, and a 400-hour standby battery life. The 1100 introduced a flashlight as well, which the user turned on and off by holding down the “C” key.
Where most device makers at the time were worried about camera megapixels and color screens, Nokia had leapfrogged its competition with a back-to-basics phone that could survive the rain, endure unreliable power grids, and light the way home.
On 9 January 2007, at the Macworld conference in San Francisco, Steve Jobs made a characteristically bold claim. “Today, Apple is reinventing the phone,” he said, soon pulling one of the first iPhones out of his pocket.
Apple CEO Steve Jobs famously launched the iPhone at the Macworld Conference in San Francisco on 9 January 2007. Nokia held a rapid-response meeting to the event the following day. Tony Avelar/AFP/Getty Images
Rumors of Apple entering the phone market had swirled since the iPod’s debut in 2001, but nobody had really reckoned with what that might mean.
“Executive summary: Apple iPhone is a serious high-end contender,” read a slide from a Nokia internal meeting held the day after Jobs’s keynote. (That slide is now in the company’s online archives, opened to the public last year.)
“User interface has been a big strength for Nokia,” it continued. “Nokia needs to develop touch [user interface] to fight back.”
Peter Bryer, at the time Nokia’s manager of strategic foresight, was part of that 10 January meeting, and he recalls that Jobs’s announcement wasn’t unexpected. But the iPhone’s extensive reliance on multitouch—save for a single home button on the front—did surprise the team.
Nokia was already aware of multitouch technology, Bryer notes. In 2006, the U.S. computer scientist Jeff Han had given a celebrated TED talk about it, demonstrating a multitouch screen, which could sense multiple fingers on the screen at a time, not just one. Bryer remembers his colleague Timo Partanen, then Nokia’s director of market and competitor analysis, getting excited about Han’s demo.
In 2006, the NYU research scientist Jeff Han showed off a new multitouch interface technology as part of a popular TED talk. By the end of the decade, multitouch—in which multiple fingers can interact with a touchscreen at once—would play a key role in smartphones from Apple, HTC, and Palm. Steve Jurvetson/Flickr
“Timo burst into the room, saying, ‘You’ve got to see this TED video of this guy using multitouch,’” Bryer recalls. “We both thought that was cool and that’s the future. Then I looked at the sponsors of the presenter’s research, and among them were Nokia and Microsoft.”
And yet it took Nokia years to develop a phone that used multitouch. “Remember, Nokia is based in Finland,” he says. “It’s very cold in Finland. They wear gloves for six months of the year, including the executives. They didn’t think a device like that would work.”
Winter gloves were no obstacle to operating the chunky buttons on Nokia phones, a design priority perhaps stemming from the company’s Finnish culture and headquarters. Erol Gurian/laif/Redux
Partanen was also at Nokia’s post-iPhone launch meeting, and recalls that there was little concern in the room. “We felt okay,” he says. “This is yet another competitor launching a great product. But we had no doubt that, if it’s successful, we would do the same. We will launch similar products.”
In November 2008, Nokia released its first touchscreen phone, the 5800 Xpress Music, a year and a half after Apple had launched its iPhone. Shaun Curry/AFP/Getty Images
That similar product ended up being the Nokia 5800 XpressMusic, known as the Tube, released in 2008. “The idea was to focus on streaming videos and television,” Partanen says. “So we made a phone with a similar form factor to the iPhone [that was] optimized for streaming content.” But the 5800 was “delayed, delayed, delayed, delayed,” he says. “It didn’t materialize in the way it was planned. It was released as a watered-down version.”
Critics skewered the 5800’s “outdated” feature set and “ancient” S60 operating system, which ran on top of Symbian OS, an open-source mobile platform Nokia had recently acquired. The 5800 sold reasonably well for its time, reaching around 8 million units in its first year alone. But it did not feature multitouch.
“I think that started to be the point when everybody realized that, hey, this is by far more difficult than earlier competitive issues we’ve had,” Partanen says.
Nokia finally released its first device with multitouch in 2010, three years after Jobs’s splashy iPhone announcement and four years after Han’s TED talk demo.
Nokia had long owned the low end of the cellphone market, with its sturdy, no-frills devices suited for that segment. So the years immediately following the iPhone’s launch saw the Finnish firm continue to thrive as it kept turning out simple, rugged devices.
As one review of the Nokia 1200—successor to the 1100—put it in October 2007, “This handset chucks away all the fancy features you’ve come to expect on a modern mobile, leaving you with a pared-down feature set that’s easy for tech novices to get their heads around.”
Two cellphone users in Nairobi, Kenya in 2013 exchange a payment on a Nokia 1200 phone via the M-Pesa Mobile Money Market, a popular online banking service. Trevor Snapp/Bloomberg/Getty Images
The 1200 kept the 1100’s dust-proofing, flashlight, and long-lasting battery, and added features aimed squarely at the developing world. The 1200 was the first to include call-time tracking and a multiuser phone book, allowing owners who planned to lend their device to set up call limits based on time or cost. This feature helped enable what Nokia researchers called kiosks—informal pay-per-call services, in which an enterprising phone subscriber charged neighbors and family members by the minute for use of the device.
In 2006, Nokia studied how Ugandans used their Nokia phones in rural and remote areas. An internal company slide deck from the time reveals just how keyed-in Nokia was to its lowest-income users. “Village phone operators are often women,” the slide deck notes. “And there tend to be a lot of children around. (Phones need to suffer considerable abuse from chewing, dust, sweat, etc.)”
“A unit of phone time is 60 seconds,” another slide states. “But to avoid accidentally going over that time and incurring extra costs, kiosk operators shorten the unit to 57 seconds, allowing a three-second margin of error. Shared mobile used as phone kiosk must show call time.”
Nokia’s familiarity with its market couldn’t protect the company forever, though.
Nokia sought out user input around the world for the company’s device designs, including hosting “Open Studio” contests soliciting users’ sketches of their dream cellphone. Shaul Schwarz/Getty Images
That’s because the iPhone wasn’t Nokia’s only looming smartphone competitor. In September 2008, the first Android phone went on sale—the HTC Dream, which was also sold as the T-Mobile G1.
While the iPhone was aimed mostly at early adopters and affluent users who could afford to drop hundreds of dollars on a new phone, Android phones were, within a couple of years, aiming at the same low-cost, global user base Nokia was selling to.
“I think it’s fair to say Android is the one that disrupted the market more for Nokia,” Bryer says. “Most of Nokia’s successful devices were not on the high-end market. But then, when Android came along, it started to fill that lower end and eventually took that market away from us.”
An executive from Nokia India in 2010 holds the company’s 5530 Xpress Music and 5230 phones, both of which had touchscreens, although only the 5530 had Wi-Fi. Sam Panthaky/AFP/Getty Images
With two emerging competitors in the low end and high end, the Finnish device maker responded with a device that split the difference—and satisfied neither camp.
Released in 2009, the Nokia 5230 attempted to be a low-priced, touchscreen (though not multitouch) competitor to both the iPhone and Android. It sold an impressive 150 million units, doing especially well in developing countries.
But the 5230 didn’t have Wi-Fi—one of the biggest complaints at the time. In the developing world, Wi-Fi connections were still rare, so the lack of Wi-Fi made some sense. But the rest of the world was not pleased.
“We had such a big gap and dominant position,” Bryer says. “Which does maybe create a level of comfort which you should never get.”
By the beginning of the 2010s, Nokia could have still drawn from the company’s labs, which were regularly spinning out new technologies and innovations. However, the Finnish handset maker ultimately failed to turn its R&D into viable new product lines in response to the emerging smartphone threat.
Nokia’s predicament had precedent—Kodak, dominant in film photography, had actually invented the digital camera in 1975 but failed to commercialize it before digital imaging made its core business obsolete.
“The technology coming from our R&D teams was cutting edge,” says Gordon Murray-Smith, director of services and ecosystems intelligence from 2008 to 2011. He recalls attending annual R&D innovation days that showcased work on self-healing materials and flexible screens, long before those technologies were seen elsewhere. “But why was Nokia not able to commercialize some of that really interesting and innovative activity more than it did?”
Nokia desperately needed an injection of life to change its fortunes. The company’s first non-Finnish CEO, Stephen Elop (a Canadian fresh off a two-year stint on Microsoft’s leadership team), did not mince words.
In an internal memo from February 2011 that was soon leaked to the media, Elop wrote, “The first iPhone shipped in 2007, and we still don’t have a product that is close to their experience. Android came on the scene just over two years ago, and this week they took our leadership position in smartphone volumes. Unbelievable.”
In 2011, Nokia released the N9, a smartphone with a Linux-derived operating system. Within a year, Nokia had pivoted toward its Windows Phone-powered line of Lumia devices. Munshi Ahmed/Bloomberg/Getty Images
Elop oversaw the 2011 launch of a Linux-based smartphone, the Nokia N9. The N9 ran on a distribution of Linux called MeeGo. Reviewers at the time praised the new smartphone direction the Finnish phone maker had taken. “Possibly the most beautiful phone ever made,” wrote one reviewer about the N9 for Engadget.
But the N9’s accolades did not ultimately carry the day. Nokia announced its Lumia line of phones the same year—a direct pivot away from MeeGo toward the Windows Phone. It would be the last major strategic turn Nokia would take as a cellphone manufacturer. From this point forward, a succession of C-suite decisions all but sealed the fate of Nokia’s iconic line of phones.
In 2013, Microsoft announced its bid to acquire Nokia’s handset operations. After the sale went through the following year, it rebranded the division Microsoft Mobile. But the year after that, Microsoft decided it had made a costly mistake, writing down $7.6 billion—nearly what it paid for Nokia’s handset division—and laying off nearly half of the former Nokia staff it had inherited.
In 2016, Microsoft sold its feature phone assets to HMD Global. The latter still sells Nokia-branded phones—budget-friendly devices as well as nostalgia reproductions of models from Nokia’s glory days. What remained was a brand name, some intellectual property, and two decades of hard-won lessons about what it takes to stay on top—and what it costs when you can’t.
“When you look at the players in the world of smartphones today, any of those players would struggle ever to achieve 14 consecutive years of being No. 1,” says Murray-Smith.
Partanen says there was a downside to Nokia’s mobile-phone dominance. “Often, being the first mover is not necessarily the best position,” he says. “Being a quick follower is the best position.”
The company itself ultimately survived, even if the transition wasn’t painless. Nokia’s revenues, which peaked in 2007, fell sharply through the mid-2010s before its new business line—telecom infrastructure—took off. Nokia now ranks among the world’s top three suppliers of 5G network equipment, serving carriers across more than 125 countries, alongside Ericsson and Huawei. Although the company could never quite crack the smartphone, it now plays a key role in providing the network backbone those smartphones run on.
2026-07-13 18:19:51
This article is brought to you by X Square Robot.
Large language models gave artificial intelligence a working recipe. Pretrain a large model on broad data, and general capability follows. Robotics has no such recipe. Robotics systems have long been assembled from separate perception, planning, and control parts that rarely add up to intelligence a robot can carry from one task to another, or one machine to another. The central problem in embodied AI is to find the equivalent recipe, and the field does not yet agree on what it is.
X Square Robot, a Chinese embodied-AI company, has made an unusually explicit bet. It argues that the recipe is an integrated stack, spanning the data a robot learns from, a world model for predicting changes in the physical world, and an action model that brings together perception, planning, reasoning, and decision-making to generate executable robot behavior. The company also believes that the stack should be built and released in the open.
X Square Robot shares its vision of bringing robots into real homes.X Square Robot
What holds the stack together is a small set of principles rather than a single overarching model.
These principles make the layers interdependent, since the same robot-free data that trains the action model is also structured to feed the world model. It is worth being precise, though. The company describes the world model and the action model as complementary but independent model families that share a code base. Both sit within its broader World Unified Model, which it has presented as an architecture for training vision, language, action, and physical prediction together.
For the X Square Robot team, one of the biggest constraints on general-purpose robots is the cost and quality of interaction data, not the number of parameters. To address that, the company built its Universal Manipulation Interface (UMI) data collection system, QUANXTA Zero Series. It works by collecting demonstrations from people wearing a rig with dual grippers rather than teleoperating a robot. This approach is not itself new, and builds on established methods for robot-free data capture. What sets it apart are two engineering choices.
X Square Robot emphasizes data quality control, recording trajectories and replaying them on a real robot, with only those that actually complete the task counted as valid.X Square Robot
The first is quality control, and it is the most distinctive part. Rather than accepting recorded trajectories as they are, the system runs a closed inspection loop, and its notable step is physical playback. A sample of trajectories is replayed on the real robot, and only those that actually complete the task count as valid. That makes the validity rate a measured quantity rather than an assumption. For example, a gripper that closes a fraction of a second too early still looks like a grasp in the data, yet it has pushed the object away, so it shouldn’t be classified as valid. A smaller clean dataset can be worth more than a larger noisy one.
The second choice is how lower-cost human data and scarce robot data are combined. The company pretrains on a large volume of robot-free demonstrations to build general representations, then adds a small amount of real-robot data as an anchor to the specific machine’s dynamics. It reports that this reaches performance comparable to an all-robot dataset at roughly a 20-fold lower cost of collection, driven mainly by how much cheaper the wearable rig is than a teleoperation setup.
The resulting dataset is deliberately model-agnostic, formatted to feed both action models and world models. The caveat is that the strongest results are measured on the company’s own robots and data-collection pipelines. Broader independent testing will help confirm and extend these promising results across a wider range of settings.
In developing its world model, called WALL-WM, X Square Robot took a differentiated approach. Most action models predict a fixed-length chunk of motion from the current image and instruction. That is convenient, but it segments behavior into fixed-duration windows, so the boundaries fall where elapsed time dictates rather than where one action ends and the next begins. WALL-WM instead treats an action-grounded semantic event as its unit: a coherent piece of behavior such as reaching, grasping, or placing, something that can be named in language, seen in video, and executed as motion.
X Square Robot’s world model, called WALL-WM, treats an action-grounded semantic event as its unit: a coherent piece of behavior such as reaching, grasping, or placing, something that can be named in language, seen in video, and executed as motion.X Square Robot
WALL-WM’s design reflects a specific concern about not discarding what large video models already know. To achieve that, a text-to-video model is coupled to a freshly initialized action network that reads from the video features without overwriting them, which preserves the visual prior. From that one process, it offers two modes. An event mode runs in variable-length segments and suits reasoning over long horizons, while a fixed-length mode produces the steady, real-time output a controller needs. That places WALL-WM between mainstream chunk-based action models and pure video world models, keeping the predictive character of a world model while still yielding executable control.
In a series of experiments, the company relied on a generalization test that is more specific than most. A model trained on a limited dataset was evaluated on long-horizon tasks in unseen settings and, on the company’s real-robot benchmark, reportedly outscored baselines that had been fine-tuned on related data. That is a meaningful result if it holds. For now, it is measured on the company’s own benchmark. With the code now being released, the broader community will have the opportunity to test, reproduce, and build on them across more settings.
The action layer carries two connected ideas. The first is a requirement the company sets for itself with Wall-OSS-0.5, its vision-language-action model: The pretrained model should run on a real robot before any task-specific fine-tuning.
The interest is less in the scores than in the design behind them. The model trains three objectives together, namely discrete action tokens, language grounding, and continuous action generation. And it keeps gradients flowing through all of them rather than freezing parts of the network as some rival designs do. It’s also a more strict method, since it reports untuned behavior such as approaching, grasping, and recovering, including on a deformable task held out of training.
As part of X Square Robot’s Wall-OSS-0.5 vision-language-action model design, the pretrained model should run on a real robot before any task-specific fine-tuning. X Square Robot
The second idea is the action interface itself, called X-Tokenizer. Most systems that turn continuous motion into discrete tokens produce codes that the language model cannot interpret. X-Tokenizer reframes tokenization as learning a semantic interface, so that the top-level code stands for the intent of a motion while lower-level codes carry finer detail, all aligned with the language model’s own features.
A useful consequence is stability. Adding noise to an action barely moves the intent code, which is what lets one tokenizer to be reused across robots without re-tuning. The tokenizer inside the production action model is a related variant of this approach. Together, the two ideas give the action layer something rather powerful: capability that transfers.
X Square Robot is betting that its unique approach combining three layers, each specialized in solving a key part of the problem, will stand out from other embodied AI stacks. The physical-playback step that grounds data quality is uncommon and sensible. The reframing of world modeling around events, with one backbone serving both reasoning and control, is a genuinely distinct approach. And the pairing of a deployable pretraining standard with a tokenizer designed as a semantic interface gives the action layer unusual coherence.
X Square Robot’s valuation has climbed above 20 billion yuan (about US $2.9 billion), suggesting that investors increasingly view data infrastructure, foundation models, and scalable training systems as long-term differentiators in embodied AI.
The next phase will bring broader validation. Much of the current evidence comes from X Square’s own robots and benchmarks. With the world model code now being made public, and as the community begins to test, reproduce, and build on the work, the reported capabilities will be tested across more robots, tasks, and settings.
X Square Robot’s recent funding rounds reflect similar confidence. The company’s valuation has climbed above 20 billion yuan (about US $2.9 billion), suggesting that investors increasingly view data infrastructure, foundation models, and scalable training systems as long-term differentiators in embodied AI.
To learn more about its future plans, the following Q&A with the X Square Robot team further explores the company’s technology, strategy, and vision.
What made now the right moment, technically, to commit to this stack? What recently became possible that wasn’t possible a couple of years ago?
It is not one breakthrough but several trends maturing together. Foundation models gave us a shared representation across vision, language, and action, so we can model what a robot sees, what it is asked to do, and how its actions change the world in one framework, rather than as separate perception, planning, and control modules.
Compute and infrastructure are finally sufficient for large-scale pretraining over long-horizon, multi-embodiment data. Just as importantly, we realized that data, not model size, is the real bottleneck for general robots—what is scarce is diverse, high-quality, reproducible interaction data. And world modeling has become practical. The useful question is no longer how to predict a few seconds of video, but how to understand the ways actions change objects, contacts, and task states. Two years ago these ingredients existed separately. Today they are mature enough to work as one system.
“We realized that data, not model size, is the real bottleneck for general robots—what is scarce is diverse, high-quality, reproducible interaction data. And world modeling has become practical.”
Your data system captures demonstrations with a wearable VR rig and custom grippers rather than teleoperating robots. What was wrong with standard teleoperation?
Teleoperation is built around controlling the robot. It forces the operator to work within the machine’s kinematics, latency, and viewpoint, and the resulting demonstrations are slower, stiffer, and less diverse. We built our system around capturing human skill instead. Manipulation is really about contact, timing, finger coordination, and recovery, not just the path the hand takes, and a wearable rig records those before the behavior is compressed onto one particular robot. It also breaks teleoperation’s expensive scaling law, in which every demonstration needs a robot.
People can generate rich data independently of any robot, and the crucial property is that those demonstrations can still be replayed and executed on a physical robot through the model. Mobility is convenient, but that replay is the real point, because it is what lets the same data be reused across different platforms.
In X Square Robot’s approach, demonstrations can be replayed and executed on a physical robot through the AI model, allowing the same data to be reused across different platforms.X Square Robot
X Square Robot reports that its pipeline has roughly an 85 percent data-validity rate. Why is quality control such an underrated bottleneck?
Because errors in robot data are far more expensive than in language data. A small timing or contact error can change what a demonstration means. If a gripper closes a fraction of a second too early, the motion still looks like a grasp, but physically it has pushed the object away. A dataset that mixes failures and accidental successes teaches ambiguity, not skill, because the real unit is the interaction, not the trajectory.
So we run automated inspection, kinematic checks, and physical replay, where we play a sample of trajectories back on the real robot and count only the ones that actually complete the task. Data quality sets the ceiling on how good a policy can be. In our experience a smaller, cleaner dataset often beats a much larger, noisier one, which is why we treat quality control as part of the model, not a preprocessing afterthought.
The model runs in both “event mode” and “chunk mode.” When does each matter?
Both matter, for different reasons. The physical world changes through events—when contact occurs, a grasp forms, or an object slips—not in fixed-frame windows. Event mode concentrates the model’s attention on those moments, and it matters most for long-horizon tasks, like clearing a table, where progress is a sequence of semantic events rather than a smooth stream. It runs in variable-length segments that follow the task rather than a clock. Chunk mode matters for deployment. Real controllers need a stable, real-time interface, and fixed-length chunks integrate cleanly with existing control systems.
We organize learning around events in the first place because a fixed window can split one motion in half or merge two together, which turns training into short-horizon pattern matching and weakens the model on long tasks. So the world model’s job is to connect event-level understanding, which is where the reasoning happens, with a fixed-length output a real robot can actually run.
Why make “deployable before fine-tuning” the criterion?
Pretraining should produce capability, not just a good starting point. If a model is only useful after heavy fine-tuning, then most of the intelligence still lives in the downstream supervision, not in the foundation model. Deployable before fine-tuning is a more honest test of what pretraining actually learned. A well-pretrained robot should already know how to approach, grasp, move, avoid obstacles, and correct itself. Fine-tuning should adapt it to a specific task or robot, not create the ability from nothing. It is also a practical requirement. A robot in a home or a workplace shouldn’t need a brand-new dataset and a new policy every time the task changes, so a foundation model that already carries general skill, and some ability to recover, is the minimum bar for something genuinely useful in the real world.
What is the most challenging part of cross-embodiment learning?
Robots differ in control frequency, delay, compliance, sensing precision, and contact dynamics, so the same instruction can require different action decompositions and recovery strategies, and a behavior that works on one arm cannot simply be copied to another. Cross-embodiment learning needs an intermediate abstraction, lower than language but higher than joint angles: how you approach an object, how you make contact, how you apply force, and how you recover from a mistake.
When we say cross-embodiment, the main capability we mean is multi-embodiment generalization: transferring across robots, training on many embodiments at once, and adapting to different kinematics. Human-to-robot transfer and other techniques are specific approaches to that goal.
“A robot in a home or workplace shouldn’t need a new dataset and policy every time the task changes. A useful foundation model should already carry general skills and the ability to recover.”
What would you most like to see other researchers attempt to reproduce or stress-test?
Three things, above all. Whether event-level representations really generalize beyond our own datasets, across more tasks, scenes, objects, embodiments, and failure conditions. Whether pretraining stays effective on robots the model never saw during training, or whether its capability is still too tightly coupled to what it has already seen. And whether real-robot evaluation can become a shared language for the field, so that we compare not just success rates but the reasons systems fail, where an instruction was misread, where perception broke down, or where recovery fell short. Robotics has been driven too often by impressive demonstrations, and real progress comes from results that are reproducible and diagnosable.
What capability is still missing before robots become dependable in homes?
Benchmarks measure competence, like whether a model can finish a task. Homes demand reliability, safe and consistent operation over time in a place that changes every day, with objects moving, instructions that are vague, and people interrupting. The missing piece is not a higher one-time success rate: it is robust recovery. A dependable home robot has to know when it is uncertain, when to slow down, when to ask for help, and how to bring the world back to a safe state after it drops something or misunderstands a request.
In a real home, failure recovery matters more than raw success, because the home does not reset itself. Homes also demand careful personalization, learning a household’s routines and preferences over time, with safety and trust as first principles. That combination, not any single skill, separates a capable demonstration from a robot people can live with.
X Square Robot’s approach is that, in a real home, failure recovery matters more than raw success, because the home does not reset itself and it demands careful personalization, with safety and trust as first principles. X Square Robot
How do the open-source components fit into X Square Robot’s World Unified Model direction?
We see these releases as layers of the World Unified Model direction rather than isolated projects. Wall-OSS-0.5, the action model, asks whether an open vision-language-action model can gain directly measurable capability from large-scale pretraining, so it is the capability layer. WALL-WM, the world model, asks how a robot should understand change in the world, shifting from fixed windows to event-level modeling, so it is the representation layer. The data system supplies the interaction data that both of them learn from.
Together they form a loop in which models produce capability, world models organize understanding, and the open-source community drives reproduction and improvement. World Unified Model is the broader architecture those layers support, bringing vision, language, action, and physical prediction together.
We are releasing these pieces openly because embodied intelligence cannot be solved by one organization; it needs many embodiments, many real tasks, and broad feedback, and the long-term goal is a stack that keeps learning and ultimately moves robots from laboratory demonstrations toward reliable everyday use.
2026-07-13 18:00:01
A practical educational guide to common and uncommon VHF propagation modes, covering thephysics, range implications, and real-world behaviors engineers need to understand.
What Attendees will Learn
2026-07-11 02:00:01
The computing community recently lost one of its enduring voices: IEEE Fellow Peter G. Neumann. The renowned computer scientist and respected risk analyst died on 17 May at the age of 93.
For almost 70 years, Neumann shaped the computing field through his pioneering work on risks, system dependability, security, and fault tolerance with rare intellectual depth and unwavering ethical clarity.
Five of those decades were spent as a principal scientist at SRI International in Menlo Park, Calif., where he worked until his death. A detailed narrative of his work, life, and mentoring is available on his SRI web page, where he chronicled his journey.
He possessed a rare ability to identify systemic vulnerabilities long before they became widely recognized. He cautioned that interconnected systems, if poorly designed or insufficiently scrutinized, could fail and become targets for exploitation. He insisted innovation always must be accompanied by responsibility, reliability, and a clear understanding of the risks involved.
With the widespread adoption of computing, information technology, artificial intelligence, and autonomous systems, Neumann’s insights have become more relevant.
Neumann was born on 21 September 1932 in New York City. After graduating from high school, he pursued a degree in mathematics at Harvard, where he had a conversation that shaped his approach to research, according to the Association for Computing Machinery (ACM). In November 1952 he had a two-hour breakfast meeting with Albert Einstein, at which they discussed the importance of simplicity in design.
Neumann was among the first generation of Harvard students to program computers and, remarkably for that era, enjoyed exclusive access to the computing systems.
After earning his bachelor’s degree in 1954, he continued his education at Harvard, earning a master’s degree in 1955. In 1958 he moved to Germany to become a doctoral student at the Technical University of Darmstadt as part of the Fulbright program, which provides funding for U.S. citizens to study or teach abroad. He earned his doctorate in 1960.
After returning to the United States, he joined Bell Labs in Murray Hill, N.J., where he worked on error-correcting codes and survivable communications. He also pursued a second Ph.D. in applied mathematics and science at Harvard, achieving that goal in 1961.
Four years later, he was assigned to work on Multics, which became an influential operating system that shaped modern secure computing architectures. Multics was a mainframe time-sharing system designed to serve the diverse needs of multiple users simultaneously. Neumann designed its filing system, which featured hierarchical directories, access control lists, and dynamically paged virtual memory segments. He also played a key role in the design of its input/output system.
In 1970 he left Bell Labs to join SRI.
Neumann made several seminal and foundational technical contributions while at SRI, including the following:
His contributions are united by a simple but profound principle: Security should be foundational, not incidental. Neumann argued that security must be embedded into system architecture from the start—not patched after deployment.
Neumann’s other enduring contribution was the creation and stewardship of the ACM Risks Forum, formally known as the Forum on Risks to the Public in Computers and Related Systems. For decades, it was one of the most respected online arenas for critical reflection on computing failures, vulnerabilities, security breaches, unintended consequences, and emerging technological threats. He transformed the forum into a scholarly archive of cautionary lessons in computing failures and risks.
In 1985 he started documenting how technological systems fail when complexity exceeds understanding and when society places blind trust in automation. He then moderated the community for 41 years, leaving his position in April, weeks before his passing.
In 1995 he published Computer-Related Risks, a book that serves as a case-driven guide to how computer systems fail and why. It is still relevant in an era defined by AI, growing cyberthreats, and our deep digital dependence.
Neumann viewed computing not as an abstract technical pursuit but as a profoundly human enterprise carrying societal responsibilities. He was thoughtfully skeptical, questioned assumptions, and challenged complacency. His observations often anticipated challenges years before they became mainstream concerns.
He exemplified high scholarship ideals and was intellectually honest and ethically steadfast. He had been a frequent critic of lax attitudes the industry has maintained toward both computer security and individual digital privacy. He warned against the industry’s tendency to repeat mistakes.
Neumann’s signature contribution was not technical but a stance. He insisted, against industry custom, that recurring computer failures were not unfortunate accidents but rather were predictable consequences of how systems were built and sold.
He was fundamentally an optimist about what can be done with research and was a pessimist about corporations.
Security is not merely a technical patch, he said, but is a systemic property requiring sound design, governance, and human judgment. He consistently warned that uncontrolled complexity is itself a source of risk.
His signature contribution was not technical but a stance. He insisted, against industry custom, that recurring computer failures were not unfortunate accidents but rather were predictable consequences of how systems were built and sold.
Neumann was honored with a number of honors including the Electronic Privacy Information Center’s 2018 Lifetime Achievement Award, the Computing Research Association’s 2013 Distinguished Service Award, and ACM’s 2005 Special Interest Group on Security, Audit, and Control Outstanding Contributions Award.
In addition to being an IEEE Fellow, he was a Fellow of ACM, the American Association for the Advancement of Science, and SRI. In 2012 he was inducted into the Cyber Security Hall of Fame.
Neumann’s greatest legacy is not necessarily his inventions but his way of thinking. His longtime interest was the risk ecology of computing—the business, technological, social, political, and personal risks that computing has created, along with its tremendous benefits in each of those spheres. He left us a timely lesson: Innovation must be accompanied by responsibility, foresight, and care.
Neumann was “one of the last of the old guard and a pointer to the future,” observed IEEE Life Fellow Whitfield Diffie, who helped invent public key cryptography. Highlighting both the significance and enduring relevance of Neumann’s work, a tribute by blogger Phoenix AMTD aptly said: “He spent 70 years cataloging how computers fail. We spent 70 years not listening. Maybe now we will.”
Let’s honor Peter G. Neumann not merely by remembering his advice but by following it.
2026-07-09 18:00:03
An examination of how satellite vulnerabilities, modern wideband waveforms, and automatic link establishment are driving renewed military and government investment in HF communications.
What Attendees will Learn
2026-07-09 02:00:01
Working in isolation, especially for leaders, is rapidly becoming an outmoded idea. The modern era is defined by rapid technological advancements and increasingly complex, collaborative global challenges. In this environment, leadership can no longer be approached as an individual pursuit.
Instead, leadership must be a collaborative effort in which knowledge, responsibility, and innovation are continuously exchanged across teams, roles, and areas of expertise. Success depends on the ability to foster connection, leverage diverse perspectives, and work collectively toward shared outcomes.
The shift is especially important in science, technology, engineering, and mathematics fields.
IEEE is bringing together emerging professionals and established experts and leaders at the inaugural IEEE International Leadership Conference to address the need for cross-generational knowledge-sharing and to equip professionals with tools for collaborative leadership. Honoring Expertise, Accelerating Potential is the theme of the ILC, scheduled for 3 and 4 October in Budapest.
The conference is expected to focus on how leaders can share information across roles, adapt to rapid technological advancements, and build stronger, more connected professional communities. Through discussions, panels, and interactive sessions, attendees can examine how collaboration across experience levels and disciplines can strengthen decision-making and foment innovation.
“There are several factors driving this shift [in leadership], including accelerating technological development cycles, the need to build public trust, and the large percentage of the STEM workforce approaching retirement,” says Vickie Ozburn, conference cochair. “Progress in STEM now depends less on individual brilliance and more on the ability to transfer knowledge, adapt, and make decisions that integrate technical expertise with ethical and social considerations.”
Instead of traditional corporate models rooted in hierarchy and individual advancement, a more dynamic framework is taking shape, one that views leadership as a shared ecosystem built on mentorship, continuous learning, and intentional knowledge transfer.
It means recognizing that professional development is no longer a one-directional flow of experience from senior professionals to newcomers. Instead, it thrives as a multidirectional exchange. When emerging professionals, mid-career managers, and seasoned experts including retirees are brought together, the result is not only richer dialogue but also more resilient and well-informed decision-making. A cross-generational dialogue enables organizations to honor what has worked, critically assess what has failed, and thoughtfully shape what needs to evolve.
Howard Wolfman, cochair of the IEEE ILC, underscores the importance of historical perspective in leadership development, invoking George Santayana’s enduring insight: “Those who cannot remember the past are condemned to repeat it.”
“In STEM especially, this principle carries significant weight,” says Wolfman, an IEEE life senior member and the founder and principal of Lumispec Consulting, in Northbrook, Ill. “Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.”
That perspective reinforces the need for greater collaboration across roles and experience levels, ensuring that knowledge is not lost and is continuously built upon and applied in new ways. In this way, leadership development becomes a continuous, interconnected process rather than a series of isolated stages.
STEM careers are no longer defined by linear progression but by evolving contributions, in which each phase adds value to the field’s broader advancement.
Adopting a new leadership paradigm requires a shift in mindset across all levels. For senior leaders, success is defined not only by what they have built but also by the people they mentor and the knowledge they pass forward. Their legacy lies in enabling future leaders to succeed.
For emerging young professionals, innovation becomes more informed and impactful when it is grounded in historical context and informed by those who have already navigated similar challenges.
“Technological innovation doesn’t happen all of a sudden; it builds on decades of research, lessons learned, and accumulated knowledge. When leaders actively connect insights from across experience levels, they gain a more complete understanding of both opportunity and risk.”—Howard Wolfman, cochair of the IEEE International Leadership Conference
For organizations, cross-generational collaboration should be recognized as a strategic advantage, not merely an aspiration. Creating environments where knowledge flows freely and diverse perspectives are actively integrated is essential for long-term success.
The evolution reframes the distinction between management and leadership.
“A leader does the right thing, and a manager does things right,” Wolfman says. As the environment continues to shift, doing the right thing increasingly depends on drawing insights from across generations and experiences.
To build leadership pipelines capable of sustaining innovation and trust, organizations must begin asking more intentional questions:
Ultimately, leadership cannot be tied solely to titles or tenure. It is about contributing to a continuum in which each generation strengthens the next.
The IEEE ILC attendees are likely to leave the event with new insights and with a transformed perspective: Leadership is not about waiting for advancement or recognition; it is about engaging in an exchange of knowledge, responsibility, and vision, where the strength of the whole depends on the contributions of every generation.
Registration for the conference opens soon.