TECH

Human learning is an underestimated factor in the success of human–AI teams

Humans collaborating with AI hold the promise of achieving superior outcomes compared to either acting alone (i.e., human-AI synergy). However, the conditions that facilitate such synergy when humans are advised by AI are not well understood. A recent meta-analysis showed that, on average, human-AI combinations do not outperform the better individual agent. We argue that this pessimistic conclusion arises from insufficient attention to human learning in experimental designs. To substantiate this claim, we reanalyzed all 74 studies included in the original meta-analysis and found that most previous research overlooked design features that foster human learning (e.g., outcome feedback to participants). Our reanalysis further revealed that studies providing outcome feedback show tentatively higher synergy than those without outcome feedback. 

Crucially, feedback paired with AI explanations was associated with positive synergy, while explanations without feedback were associated with negative synergy-suggesting that explanations improve synergy mainly when humans can learn to verify the AI's reliability through feedback. Our reanalysis suggests that the current literature underestimates the potential of human-AI collaboration because it predominantly relies on paradigms that do not facilitate human learning, thus hindering humans from effectively adapting their collaboration strategies. However, experiments directly varying learning opportunities are needed for stronger, causal conclusions. We advocate for a paradigm shift in human-AI interaction research that explicitly addresses human learning and thus enhances our understanding of and support for successful human-AI collaboration.

New study suggests that human–machine interaction is better than its reputation. Human–AI collaboration is widely seen as the work model of the future: The expectation is that together, humans and AI will be more productive than either working alone. Yet the research to date seemed to tell a different story: On average, human–AI teams did less well than the better of the two alone. A reanalysis led by researchers at the Max Planck Institute for Human Development now suggests this verdict was overly pessimistic. 

“The potential of human–AI collaboration is underestimated.” That is the central conclusion of the new study published in the Proceedings of the National Academy of Sciences, and conducted by an international team led by Julian Berger and Stefan Herzog from the Center for Adaptive Rationality at the Max Planck Institute for Human Development. The research team took a closer look at a review study that made waves in 2024. The review analyzed 74 studies on human–AI interaction, spanning a wide range of tasks from classifying images and predicting house prices to writing code. 

Its findings were sobering: On average, the synergy effect was negative. In other words, the human–AI team performed worse together than the better of the two working alone. This fueled doubts about whether human–AI collaboration was worth pursuing at all. 

Learning effects underestimated...Is the situation really that bleak? That is the question the researchers set out to answer in their new review. They revisited the 74 studies, examining the nature of the human–AI interaction in more detail. They wanted to know whether the picture would look any different if AIs explained their results or if humans were given feedback on what the right answer would have been after each decision, giving them the chance to learn and adapt to the algorithm. 

The first surprising result was that most studies did not offer learning opportunities. Only 10 of the 74 gave participants any feedback at all, and none varied it experimentally. “Learning effects in human–machine interaction remain virtually unexplored,” says Julian Berger, who explored this topic as part of his doctoral dissertation at the Max Planck Institute for Human Development. 

 To tease out these effects, the researchers dug deeper. Using a statistical method known as meta-regression, they compared studies with and without feedback, factoring in whether the AI explained its recommendations. And here came the second surprising result: When participants had access to both feedback and explanations, the human–AI tandem tended to outperform the better partner working alone. On the other hand, explanations alone—without feedback—were associated with a distinctly negative synergy. 

What appears to be the key is a combination of explanation and feedback. Feedback alone—without explanations—made no clearly discernible difference. This result suggests that “explanations improve synergy mainly when humans can learn to verify the AI’s reliability through feedback,” the researchers write. 

Open questions: causality and generative AI...Because none of the 74 studies varied the learning opportunities provided within an experiment (e.g., feedback vs. no feedback), the studies were compared based on their design features. The researchers stress that experiments directly manipulating learning opportunities are needed for stronger, causal conclusions to be drawn. Another open question is how the findings translate to generative AI and large language models (LLMs). “Verbal interaction with chatbots could make it easier to provide people with personalized feedback and explanations, thereby stimulating learning. But for that to happen, people also need to find out what is actually correct; a chatbot’s answers can be wrong and do not constitute independent feedback,” says Stefan Herzog, Senior Research Scientist in the Center for Adaptive Rationality at the Max Planck Institute for Human Development. “At the same time, new risks arise from cognitive ‘offloading’—that is, delegating mental work to AI. In the medium and long term, this could jeopardize learning success or even lead to loss of skills.” 

Factoring human learning into AI implementation...To get a clearer picture, the researchers call not only for further experiments but, above all, for “a paradigm shift in human–AI interaction research that explicitly addresses human learning.” They emphasize that “supporting human learning—particularly by providing outcome feedback and AI explanations—is an underappreciated and understudied but promising lever for increasing human–AI synergy. Our findings paint a more optimistic picture and highlight the need to explicitly and experimentally study when and how fostering human learning boosts human–AI synergy.” 

The research already supports a clear recommendation: AI is a tool, and like any tool it must be designed with humans in mind from day one. “When designing AI systems and interactions with them, we need to consider humans as learning beings from the outset,” Herzog stresses. Only then can the full potential of the interaction be realized. 

Earlier analyses of human–machine interaction mostly showed negative synergy effects. A review of 74 studies suggested that human–AI teams perform less well than the better of the two working alone. 

These 74 studies have been reanalyzed by researchers from the Center for Adaptive Rationality at the Max Planck Institute for Human Development. Their results paint a much more positive picture. 

The reason for the difference: The studies systematically underestimated human learning ability; most did not examine it a all. Yet when participants were able to learn from feedback and explanations, human–AI teams tended to outperform the better partner alone. 

Need for a paradigm shift: The researchers call for future work on human–AI interaction to explicitly factor human learning ability into studies and experiments. 

Human learning is undoubtedly one of the most underestimated yet critical factors for the success of teams comprising both humans and Artificial Intelligence (AI).

Organizations often focus almost entirely on training and "teaching" the AI ​​model (Machine Learning), overlooking the fact that the true effectiveness of this partnership relies on a two-way street. For collaboration to be productive, humans must also undergo a profound process of adaptation and skill development.

Human learning is an underestimated factor in human–AI teams because prior research largely ignored whether human participants could adapt, learn, and verify AI reliability through feedback.

Recent reanalysis by researchers at the Max Planck Institute for Human Development of 74 previous studies revealed key insights:

• Lack of Learning Opportunities: Out of 74 analyzed studies, only 10 provided outcome feedback to human participants.

• Overlooking Synergy Potential: Because humans could not adapt their collaboration strategies or learn how to use the tool, human–AI teams often performed worse than the best individual working alone (negative synergy).

• Misleading Explanations: Explanations from an AI without outcome feedback were actually linked to negative synergy, as users could not tell if the rationale was trustworthy.

Why human learning changes the outcome:

• Feedback + Explanations: When participants received both clear outcome feedback and AI explanations, human–AI teams regularly outperformed the better partner working alone.

• Verifying Reliability: Feedback allows humans to test and verify whether the AI's advice is accurate, building an effective working strategy over time.

• Designing for Learning: Treating humans as active learners—rather than passive recipients of AI output—unlocks true positive synergy in technology design.

🌐 Why is Human Learning Neglected?

• The "Plug-and-Play Tool Myth": There is a misconception that generative or predictive AI functions like traditional software. In reality, it behaves more like a dynamic—and sometimes unpredictable—colleague.

• Focus on the Algorithm: Millions are invested in calibrating data and training neural networks, yet almost nothing is spent on training the professionals who will interpret and act upon the technology's outputs.

📊 What Do Humans Need to Learn?

For this symbiosis to occur, the human learning curve must address three key pillars:

AI Mental Models Understanding what AI can and cannot do, as well as its biases and limitations. Avoiding blind trust in or complete rejection of the technology.

Prompt Engineering & Dialoguen...Learning to formulate questions, provide context for problems, and refine responses. Extracting maximum value and accuracy from the tool.

Critical Interpretation Developing the ability to audit AI responses (curation). Mitigating the risks of "hallucinations" and technical or ethical errors. 🚀 The Impact of Learning on Team Dynamics

When the human element is integrated into the learning process, team dynamics shift dramatically:

1. Reduced Technology Abandonment: Professionals who understand how AI works are less likely to give up on it after the first error.

2. Co-evolution: As humans learn to guide the AI, interactions generate better data, which in turn improves the algorithm's long-term performance.

3. From Operator to Supervisor: The human role shifts from "executing tasks" to "managing intelligence," requiring greater critical thinking and decision-making skills.

Max Planck Institute for Human Development 

TECH

AI could reduce workers' bargaining power, says ECB

Artificial intelligence could diminish the bargaining power of workers in the Eurozone, even before causing significant shifts in employment. This warning comes from Philip Lane, Chief Economist at the European Central Bank (ECB), who suggests that the potential for certain roles to be replaced could weaken workers' ability to negotiate wage increases.

This assessment arrives at a time when inflation remains under pressure from energy costs, and companies are maintaining modest hiring plans. According to Lane, the threat of automation could introduce a new factor that restrains wage growth—even if artificial intelligence has not yet triggered substantial changes in overall employment levels.

How AI might affect workers' bargaining power... In a speech delivered on October 13 in Dublin, during the International AI Summit, Philip Lane explained that artificial intelligence has not yet significantly altered general employment trends in the Eurozone. Nevertheless, hiring plans remain cautious, which particularly affects individuals seeking their first job.

The economist argues that the possibility of replacing certain functions with AI systems could limit workers' ability to demand better pay. This pressure could temper wage growth and reduce the share of economic income that goes to labor.

This mechanism does not necessarily require layoffs. If workers believe their roles could be automated, they may have less leverage to negotiate salary increases or seek better conditions elsewhere.

However, the analysis does not demonstrate that this dynamic is already producing measurable effects. Lane presents it as a possibility that helps in understanding the evolution of the labor market. The official had already raised this possibility on October 5, linking the potential replacement of workers by AI to a cooling in labor demand and the difficulty in securing wage increases that could offset rising energy prices.

Half of the surveyed workers already use AI...Concerns regarding the effects of automation arise at a time when artificial intelligence is being rapidly adopted by European companies.

According to a study published by the ECB in August 2026, the share of surveyed workers who use AI in their professional activities doubled over two years—from 26% in 2024 to 52% in 2026.

These findings are based on the ECB’s Consumer Expectations Survey, which collects monthly responses from approximately 20,000 individuals across 11 eurozone countries.

Participants who use these tools reported time savings; however, these gains are concentrated in specific sectors and specialized tasks, such as programming. Despite this rapid adoption, the ECB believes it is still too early to discern any significant impact on the economy's aggregate productivity.

Furthermore, this transformation does not affect all professions in the same way. Some roles may see increased demand for skills that complement artificial intelligence, while others face a higher risk of being replaced.

This disparity underscores the importance of professional adaptation. As discussed in our article on the use of AI in the workplace, the automation of tasks does not necessarily mean the elimination of entire professions; rather, it may shift the skills required and change how companies organize work.

Investment in AI also exerts pressure on interest rates. The economic consequences of artificial intelligence extend beyond employment and wages. Philip Lane considers it plausible that the global growth in AI investment, coupled with expectations of future productivity gains, is contributing to the rise in long-term interest rates.

Developing models, building data centers, and expanding computational infrastructure require substantial investment. Financing these activities has contributed to an increase in debt within the technology sector, particularly in international markets.

Although the scale of investment in Europe is smaller than that seen in the United States and other regions, the rise in international interest rates is also spilling over into the euro area.

For the ECB, this development represents a tightening of financial conditions. Higher long-term interest rates can reduce corporate investment, increase financing costs, and dampen economic activity.

These effects are relevant to monetary policy decisions, as tighter financial conditions can help reduce inflationary pressures over the medium term.

Lane also warned of the risks associated with the high valuations of AI companies and the increased issuance of debt to finance the sector. A revision of growth expectations could affect financial markets and call into question some of the investments already made.

The ECB acknowledges the potential of AI for the European economy...Despite the identified risks, Philip Lane believes that artificial intelligence could boost productivity and help offset the economic effects of Europe’s aging population.

The technology could enable companies to produce more with the resources at their disposal, but the outcomes will depend on how AI is integrated into work processes and on the ability of organizations and workers to adapt.

The main uncertainty lies in the distribution of these gains. An improvement in productivity does not, in itself, guarantee proportional wage increases—especially if automation reduces the demand for certain skills.

For now, the ECB does not see a widespread transformation of employment driven by artificial intelligence. The possibility that the technology may be limiting workers' bargaining power remains an economic hypothesis that will need to be tested against trends in wages, hiring, and productivity.

Artificial intelligence can reduce workers' bargaining power by automating routine tasks, shifting the balance from labor toward capital, and muting hiring plans.

Key factors in AI's impact on bargaining power:

• Muted Hiring and Leverage: Slow or cautious hiring limits employees' ability to switch jobs for higher pay or use competing offers as leverage.

• Risk of Replacement: The threat of AI replacing specific worker roles lowers the pressure on companies to raise wages during high inflation.

• Shift to Capital: As firms invest more in automation and software tools, their direct dependence on individual workers decreases.

• Increased Productivity Demands: Many organizations use AI to extract more output from existing employees rather than increasing headcount or raising compensation.

• Algorithmic Management: Data-driven oversight and monitoring can intensify workloads, shrink worker autonomy, and complicate collective organizing.

Artificial intelligence reduces workers' bargaining power by shifting economic leverage toward employers through several core mechanisms:

• Threat of substitution: The potential for AI and automation to replace routine, medium-skill, or specialized human tasks makes firms less dependent on individual workers or labor supply, lowering employees' confidence and leverage to demand higher pay.

• Muted hiring and mobility: When companies scale back or freeze general hiring—relying instead on AI to extract more output from existing staff—workers have fewer alternative job openings or competing offers to use as leverage in wage negotiations.

• Algorithmic management and surveillance: Employers increasingly use data-driven systems to track productivity, set workloads unilaterally, and anticipate or disrupt union-organizing efforts, weakening collective voice.

• Shifting labor share of income: As capital is funneled into intelligent infrastructure and automation platforms rather than human wages, the overall labor share of total income shrinks, dampening wage growth even during periods of high inflation.

mundophone

 

DIGITAL LIFE

Smartphone chatting and payment habits help ai identify daily stress, small study finds

Your smartphone may be able to tell you when you are having a stressful day based on how you use it, a KAIST research team has found.

The AI-assisted technique, developed by a team at the KAIST School of Computing, assesses users' daily stress based on the everyday tasks they repeatedly perform on their phones, the university said Thursday.

The best-performing model identified stressful days with an average accuracy of 75.9 percent among participants tracked for about a month, and 73.8 percent among those tracked for about 105 days.

The research ties mental health monitoring to the smartphone, a device most people now carry everywhere. Because phone usage records capture changes in everyday behavior over time, they are increasingly used as a digital indicator of mental health conditions such as stress and depression.

Earlier studies mostly measured screen time or which apps people used, but those numbers can change whenever someone switches apps. The researchers gave the example of someone who moves from KakaoTalk to WhatsApp or Line. The app changes, but the task stays the same: chatting on a messenger.

The KAIST team instead focused on what users do on their phones rather than which apps they use, so the data stays consistent even when people switch apps.

The team analyzed data from 26 university students and office workers in their 20s and 30s, divided into 16 short-term participants, who participated for 30.5 days on average, and 10 long-term participants, who took part in the study for 105.1 days on average. The participants provided both phone data and daily self-assessments on stress, which were used to create a personal model.

The best-performing model distinguished stressful days from stress-free ones with an average accuracy of 75.9 percent for the short-term group and 73.8 percent for the long-term group.

The study showed a link between phone habit and stress, not that any habit causes it. The research also did not record what users typed or what their messages said, to protect privacy. Instead, it logged how often a person typed and how they moved between screens.

"We looked at the smartphone not simply as a device that measures usage time, but as a living space where a person's communication, information searches and financial activities repeat," said Prof. Lee Ui-chan of KAIST's School of Computing.

“By turning complex smartphone records into units of behavior that people can understand, this study lays the groundwork for analyzing long-term changes in mental health more reliably,” Lee said.

Three types of smartphone users...Participants were divided into three groups according to their habits. “Pure social” users mainly used their phones to communicate, maintain relationships, and form new social connections.

“Light process” users spent less than four to five hours a day checking news, viewing entertainment, using social platforms, and passively scrolling. About one third were classified as “heavy process” users who spent four to five hours or more on their phones each day, primarily consuming content rather than talking with others.

“Our main finding is that personality alone—such as a high level of neuroticism—does not make someone addicted to their phone. People who are more prone to negative emotions like anxiety, depression, or stress do not automatically become addicted. What really matters is how well they can control their behavior, emotions, and impulses and how strongly they fear missing out,” said Dr. Johanna Takács, senior research fellow at the Faculty of Health Sciences at Semmelweis University and lead researcher of the study.

Passive scrolling strains attention...The findings point to passive content consumption as a particularly concerning behavior. Social feeds and short videos deliver a rapid succession of novel stimuli, continually pulling attention from one item to the next. Prolonged exposure to that pattern may make sustained concentration more difficult and contribute to mental fatigue.

On cognitive tests, “heavy process” users performed worse than expected for their age on measures of attention and memory. They also slept more poorly, sought new stimulation more intensely, and found it harder to resist the urge to check their phones or satisfy an impulse immediately.

Time still mattered, but primarily for physical health. Longer use was associated with posture-related effects that may extend beyond neck discomfort to balance, reaction time, and everyday movement.

Because the research identified associations rather than assigning participants to different smartphone habits, it cannot prove that phone use directly caused the observed mental or physical differences. The results instead highlight patterns that may help identify which behaviors deserve closer attention.

How text neck affects the body...“When people hold their phones at waist or chest level and look down at them, the head moves forward, creating a forward head posture, also known as ‘text neck.’ This puts extra strain on the neck and can cause pain or early wear and tear of the neck joints. The neck contains receptors that tell the brain how the head is positioned. If a forward head posture is maintained for long periods, these signals can change and lead to balance problems. These effects are not limited to phone use but can also influence everyday movements,” said Beáta Seregély, assistant lecturer and expert responsible for the physical measurements in the study.

The head becomes harder for the neck to support as it shifts forward, increasing the load on muscles and joints. At the same time, prolonged changes in head position may interfere with sensory information that helps the brain coordinate posture and balance.

Building healthier phone habits...The researchers do not recommend banning smartphones or blaming young people for using them. Their findings instead support helping users become more deliberate about when, why, and how they reach for their devices.

Practical steps include silencing unnecessary notifications, especially alerts from social media and messaging apps; setting limits on social media use; avoiding phones before bedtime; exercising regularly; and keeping the screen closer to eye level to reduce strain.

Mental health strategies may be just as important. Behavioral training and psychological support can help young people strengthen self-control and become less anxious about missing social events. As those pressures ease, people who are sensitive, anxious, or prone to stress may be far less likely to develop problematic smartphone habits.

A new study by researchers at KAIST (Korea Advanced Institute of Science and Technology) shows that artificial intelligence can identify daily stress with up to 75.9% accuracy by analyzing routine smartphone habits rather than just total screen time:

• Focus on Intent, Not Apps: Instead of tracking specific app names (which change or get swapped out by users, like moving from KakaoTalk to WhatsApp), the AI focuses on what a user is trying to accomplish—such as messenger chatting, web browsing, financial payments, or reading and writing emails.

• SHIRBT-MMF Technology: The research team developed a system called "Smartphone Human Interaction Routine Behavior Tasks" (SHIRBTs) using a technique named Multi-Level Sequential Pattern Mining (ML-SPM) combined with a large language model (LLM) to categorize everyday screen transitions.

• Accuracy Rates:

• Reached 75.9% average accuracy for short-term participants (tracked for an average of 30.5 days).

• Reached 73.8% average accuracy for long-term participants (tracked for an average of 105.1 days).

• This performance was about 3.5 to 5.5 percentage points higher than traditional app-usage tracking methods.

• Participant Group: The study analyzed real-world data from 26 university students and office workers in their 20s and 30s using Android devices.

• Privacy Protection: The system did not store actual message text or contents; it only logged behavioral patterns (like frequency and screen transitions), and contact identifiers were processed using hashing (converting data into anonymous values).

• Important Caveat: The researchers note that the study shows an association between behavioral patterns and stress, not a direct causal relationship

A research team at KAIST (Korea Advanced Institute of Science and Technology) developed an AI-assisted technique that identifies daily stress by tracking routine smartphone usage, achieving an accuracy of up to 75.9%.

Key findings of the study:

• Accuracy: The top-performing personal AI model distinguished stressful days from stress-free days with an average accuracy of 75.9% for short-term participants (tracked for roughly 30 days) and 73.8% for long-term participants (tracked for about 105 days).

• Behaviors Tracked: The system analyzes everyday actions such as typing frequency, screen transitions, communication patterns, information searches, and financial/payment activities.

• Privacy-First Design: To protect user privacy, the AI did not read the content of text messages or record what users typed, focusing instead on behavioral metadata like movement between screens and interaction rhythms.

• Participants: The study tracked Android smartphone users—consisting of 26 university students and office workers in their 20s and 30s—who logged their phone habits alongside daily self-assessments of stress.

• Correlation, Not Causation: The researchers noted that the findings show a strong association between phone habits and stress, rather than proving a direct causal relationship where a specific habit causes stress.

by mundophone

TECH

RTX Spark laptop hands on review: The OmniBook Ultra 16 is HP's new flagship

HP has leaned into its revived “Omni” branding with two new systems to amplify the company’s mobile and desktop computing AI chops. The new HP OmniBook Ultra laptop aims to claim (however briefly) the “world’s fastest AI PC” title with a custom-design AMD Ryzen AI 300-series processor that’s faster even than AMD’s own claims for its latest silicon just a month prior. 

Meanwhile, the HP OmniStudio X all-in-one desktop comes in two sizes and combines current-generation Intel Core Ultra processing with the option for Nvidia GeForce RTX graphics. I had the chance to play with both at HP’s debut event in New York City and learn more about them.

HP’s second such laptop to bear the revived OmniBook name, the OmniBook Ultra is a performance-focused notebook that aims to reach on-trend techies with work to get done. It has high-end competition like Apple's MacBook Pro squarely in its sights, and HP has cooked up a few "firsts" to draw a bead on it.

But first, this 14-inch pseudo-workstation's recycled-metal frame, at 3.48 pounds, feels quite dense when picking it up. That isn’t necessarily bad—just don’t expect a feathery experience. Regardless, it won’t strain your backpack too heavily, weighing just as much as a 14-inch MacBook Pro with the M3 Pro chip. 

Opening the lid reveals a 2.2K (2,240-by-1,400) IPS touch screen with 100% SRGB color coverage that can shine at up to 300 nits. However, at least on paper, the MacBook Pro display still comfortably outshines this one with finer resolution, higher brightness, and broader color coverage.

The OmniBook Ultra has a starting price of $3,199 in the US—$200 more than the smaller OmniBook X, which features the same 18-core RTX Spark N1X chipset. Meanwhile, HP charges $3,619 for the 20-core Nvidia variant. In contrast, the 20-core variant can be purchased in the UK for £3,199 (~$4,235), tax included.

Alternatively, the new OmniBook Ultra can be configured with 64 GB of RAM for $4,199. At the time of writing, HP charges an extra $1,110 for 128 GB of RAM and $850 for a 2 TB PCIe Gen 5 SSD to replace the standard 1 TB drive. Consequently, a fully maxed-out OmniBook Ultra costs $6,159.

Furthermore, all SKUs include the same 99 Wh battery. HP also equips every OmniBook Ultra variant with the same 16-inch OLED display. With a resolution of 2,880 x 1,800 pixels, this screen offers a 120 Hz refresh rate, just like its 14-inch counterpart. Likewise, according to the specifications, the display reaches a peak brightness of 500 nits for SDR content and 1,100 nits for HDR content.

It is the beginning of a new era for the PC market: A new competitor is entering the historically Intel and AMD dominated field of PC processors. In this case, it is a very well known player in Nvidia, the most important manufacturer of GPUs for computers.

The launch of Nvidia's new RTX Spark platform of ARM-based Socs is close, and Nvidia has managed to get all the big PC manufacturers on board. One of them is HP, the second biggest PC manufacturer if you go by sales numbers. The American tech company is launching two devices with RTX Spark, and both are under the OmniBook brand: The OmniBook X 14 and the OmniBook Ultra 16.

Ahead of the launch, we had a chance to take a look at the bigger of the two, the new HP OmniBook Ultra 16. Unfortunately, we could only look at a turned-off sample, as Nvidia does not want any benchmarks ahead of the official release, but the physical quality of the laptop is interesting to us all the same.

HP OmniBook Ultra 16: An OmniBook that could be a ZBook...When compared with the new colorful entry-level OmniBook 5, the OmniBook Ultra 16 looks far more business-like. It is clad in an all-black aluminum chassis, with the aluminum being painted with a smooth finish as well as polished sides. The overall quality of the device is very high, thanks to the CNC unibody construction chosen, but so is the weight. With 2.2 kg, this is not a device many people will want to lug around every day, and it feels substantial in when held.

A contributing factor here is the glass front of the screen. It also makes the screen highly glossy, which is the most distinguishing factor marking this as a consumer device. In other ways, the OmniBook Ultra 16 also could be sold as a workstation HP ZBook model with no issues. Take the hinge design, for example: The hinges on the top of the base allow for big fan exhausts on the back and thus a better airflow. A negative of this design: The screen opening angle is fairly limited, to around 135 degree.

A look at the bottom of the system reveals a rather large air intake for the two fans. While we could not open up the laptop, HP says that the new OmniBook Ultra has a total of two fans and three heatpipes, enabling the 20 core version of the Nvidia RTX Spark used here to consume up to 70 W. The power is supplied by a 140 W charger or a 99 Wh battery.

The keyboard of the OmniBook looks familiar to the EliteBook X we recently tested, but it is different, with a lower key travel. But HP has changed the design, given the key-dish a slightly concave form to make it more comfortable. For the mouse, HP puts in a huge haptic touchpad, which we unfortunately could not test due to the device being switched off. As for ports, HP includes Thunderbolt as well as USB A and HDMI, as well as a microSD slot - too bad that it could not squeeze in a full-size SD card reader. At least there are four speakers, and HP managed to build a physical webcam shutter, too. That is another feature that is also very common in HP's business series.

Your AI.Running locally. Built on the NVIDIA CUDA™ platform, with up to 1 petaflop of FP4 AI performance and with 64 GB of unified memory, it lets you run larger models locally, accelerate demanding workflows, and keep your data private.

Keeps its cool when the work gets intensive. Larger heat pipes and dual ultra-thin fans efficiently dissipate heat to help sustain peak performance through demanding creative projects, AI development, and agentic workflows. So you can stay focused on creating what's next—not managing your machine's limits.

See every detail come to life.Great ideas deserve exceptional fidelity. The HP OmniBook Ultra 16 brings your work to life with a stunning 16-inch2 3K OLED6 display with VESA DisplayHDR™ True Black 1000, delivering vibrant color, remarkable contrast, and exceptional clarity so every image, design, and frame looks exactly as you intended.

Big sound. Exceptional clarity. Quad speakers powered by smart amplifiers delivering louder, fuller sound with deeper bass and exceptional clarity—even at higher volumes—so every soundtrack, voice, and creative detail sounds exactly as it was meant to be.

mundophone

 

TECH

Rowan engineers develop new way to monitor 3D printed concrete during construction

A Rowan University team of engineers has developed a new way to measure how 3D printed concrete deforms during printing and curing, revealing how lower layers respond to the weight and pressure of newly deposited material. The approach could help researchers and manufacturers better understand how printed concrete behaves during construction and what factors affect the quality of finished structures. 

Around the world, concrete additive manufacturing is increasingly used to speed construction. Rather than pouring concrete into forms, builders can use industrial-scale 3D printers to extrude the paste-like material layer by layer to create walls and foundations.  

Researchers have looked for ways to monitor what happens to concrete during the earliest stages of printing, when newly deposited layers are still deforming, curing and bearing the weight of additional material. Understanding that process could advance research aimed at improving the quality, durability and efficiency of 3D printed concrete construction. 

Led by Islam Mantawy, Ph.D., and Behrad Koohbor, Ph.D., researchers in the Henry M. Rowan College of Engineering developed and tested a new method for reconstructing how concrete changes throughout the printing process. That’s important because multiple factors, from temperature and humidity to the material’s chemical composition, can affect how a printed structure behaves as it cures. Understanding those factors and their precise effects could help engineers identify potential problems earlier and improve future concrete printing processes.     

“The idea is we need to avoid any premature failure,” said Mantawy, an assistant professor in the Department of Civil & Environmental Engineering and head of the Additive and Robotic Construction Laboratory. “If you're building an 8-foot-tall wall and the bottom layer starts to crack and collapse, the entire structure can fail before you reach the final layer. What was supposed to be a faster, more efficient construction method becomes slower and less efficient because you have to start over from scratch.”

They started with a long-established imaging technique called digital image correlation (DIC) that measures how materials and structures deform under force. Using a reference image of an existing object, researchers use cameras to take thousands of images as the material and structures respond to external stimuli such as mechanical forces, temperature change and humidity. 

In concrete printing, however, there is no reference image because the structure is being created as the process unfolds.

The team used DIC “in reverse time,” starting with images of the completed structure and working backward through thousands of images captured during printing and curing. With those images, the team was able to construct a mathematical approach to validate the reverse-time DIC method and for explaining what happens as concrete deforms, shrinks and cures, even during the first moments after a layer is printed.  

Published in the journal Experimental Mechanics, the paper demonstrates that images of a fully cured 3D printed structure can be analyzed in reverse order, using the natural surface roughness of the concrete as a tracking pattern, to reveal how the structure deformed throughout printing and curing. 

Beyond concrete printing, the researchers say DIC “in reverse time” could be useful in other additive manufacturing processes where conventional quality control methods are limited because a structure does not yet exist when production begins.

“We are literally writing the history of the process, but we start from the end and go back,” said Koohbor, an associate professor in the Department of Mechanical Engineering and affiliated faculty with Rowan’s Advanced Materials & Manufacturing Institute.  

The researchers see the work as an early step toward smarter, more autonomous forms of additive manufacturing.

“Without knowing what’s happening inside the structure, you won’t be able to achieve self-operating 3D printing,” Mantawy said. “It’s a way to have an eye on the structure as it evolves.”

From conventional construction to digital fabrication...Civil engineering is entering a phase in which construction is no longer defined only by drawings, formwork, manual placement, and conventional sequencing. Digital fabrication and robotic construction introduce a different logic: structures can be designed as data, translated into toolpaths, produced by controlled machines, monitored during fabrication, and verified through a combination of material testing, process control, and structural assessment. 

Three-dimensional concrete printing, often called 3D concrete printing or 3DCP, is one of the most visible examples of this transformation. In many systems, a cementitious material is pumped through a hose and deposited layer by layer by a gantry, robotic arm, or mobile printing platform. The printed object may be a wall, façade element, formwork, shell, bridge component, foundation element, or architectural unit. The attraction is clear: the technology can reduce or eliminate some formwork, allow complex geometries, support material-efficient design, and open new possibilities for automated construction. 

However, 3D concrete printing should not be treated as a simple replacement for conventional casting. It changes the engineering problem. The material must be fluid enough to pump and extrude, but stiff enough to keep its shape and support subsequent layers. The geometry must be printable, not only beautiful. The structure must satisfy load-bearing, durability, reinforcement, connection, fire, tolerance, and inspection requirements. The construction process must therefore be considered as part of the structural system. 

What makes 3D concrete printing different? In conventional reinforced concrete construction, the formwork defines the geometry, reinforcement is placed before casting, and the concrete fills the mould. In 3D concrete printing, the material is deposited directly in its final or near-final geometry. This creates freedom, but also responsibility. Layer geometry, printing speed, extrusion rate, nozzle height, waiting time between layers, ambient temperature, and material thixotropy all influence the final performance of the printed element.

The digital workflow is also different. A printable structure begins as a design model, but it must pass through design-for-additive-manufacturing decisions, toolpath generation, slicing, simulation, robotic control, printing, post-processing, and verification. Recent work on the digital chain in concrete 3D printing emphasizes the need to coordinate parametric design, path planning, numerical simulation, fabrication constraints, and information transfer between platforms. 

For researchers and practitioners, this means that the critical questions are not only “Can we print it?” but also “Can we design it, control it, inspect it, reinforce it, certify it, and maintain it?” A printed element should be judged by the same engineering principles as any other civil infrastructure component, while also accounting for the specific anisotropy, interfaces, geometry, and construction history introduced by layer-wise manufacturing.

The material challenge: printability is a narrow window...The central material challenge in 3D concrete printing is printability. A printable mixture must satisfy several conditions at the same time. It must be pumpable so that it can move through the delivery system without blockage or segregation. It must be extrudable so that the nozzle produces a continuous and stable filament. It must be buildable so that fresh layers can support their own weight and the weight of subsequent layers. It must also provide sufficient interlayer bonding so that the final element behaves as a coherent structural component. 

These requirements can conflict. A highly flowable mixture may be easier to pump but may deform after deposition. A very stiff mixture may keep its shape but may cause pumping problems or weak interfaces. A long delay between layers can reduce interlayer bond, while a short delay can cause excessive deformation if the lower layer has not developed enough stiffness. This is why rheology, thixotropy, setting behavior, admixture compatibility, and early-age mechanical properties are central research topics in printable cementitious materials. 

Environmental conditions matter as well. Temperature, wind, humidity, sun exposure, and moisture loss can affect open time, layer surface condition, bond quality, and dimensional stability. In practice, a printing recipe cannot be separated from the printer, nozzle, hose length, pump, printing speed, layer height, quality-control system, and construction environment.

Structural design, reinforcement, and interfaces...For civil engineering, the largest questions are not only material flow and geometric novelty, but structural reliability. Printed concrete elements can exhibit anisotropic behavior because the interfaces between layers may influence strength, stiffness, fracture behavior, permeability, and durability. The load path must be compatible with the printed geometry and the direction of layers.

Reinforcement remains a key issue. Conventional reinforced concrete depends heavily on steel reinforcement for tensile resistance, crack control, ductility, and robustness. In printed construction, reinforcement may be placed manually, inserted during printing, integrated after printing, combined with printed formwork, or replaced in limited applications by fibers or alternative reinforcement strategies. Each solution has consequences for bond, cover, durability, construction tolerance, and inspection.

Numerical simulation can help engineers understand these problems before printing. FEM-based modelling has been used to simulate fresh-state buildability, layer addition, overhang limits, and print failure modes. These tools are useful because print failure during construction can waste material and time, while validated simulations can help identify safer layer heights, printing speeds, toolpaths, and geometries. 

Quality control: from visual inspection to digital monitoring...Quality control is a major reason why 3D concrete printing should be treated as a digital fabrication process rather than only a new placement method. The printed product is defined by the material, but also by the process history. Layer width, layer height, surface texture, defects, waviness, discontinuities, dimensional deviations, and local instability can all influence performance.

Recent reviews of geometry and surface inspection in 3D concrete printing discuss image-based systems, structured light, laser scanning, point clouds, and other sensing methods for monitoring printed layers and final geometry. These methods can support quality control during printing, after each layer, before assembly, and during assembly of printed components. 

Machine learning is beginning to enter this field. Recent research has explored deep-learning approaches for predicting filament cross-sectional geometry from material properties and process parameters. Such methods point toward future closed-loop systems in which simulations, sensors, and printer control are connected to reduce trial-and-error and adjust printing parameters more intelligently. Still, these tools must be validated carefully and used within the range of the data on which they were developed.

 

mundophone

 

TECH

Tiny droplets can form soft networks for brain-inspired computing

Inside the human brain, billions of individual neurons connect to form a vast network capable of rapidly processing information and electrical signals. A team of researchers and engineers at Penn State is taking inspiration from these natural computers to build soft networks of water-based droplets, connected by membranes like those found in the walls of living cells. Their networks lay the foundation for a new kind of soft computer built for wet environments, capable of sensing and processing information with ions much like silicon chips do with electrons, but at far lower energy cost.

The team immerses tiny lipid-containing droplets in oil, where lipid molecules naturally arrange themselves around each droplet. When neighboring droplets touch, these lipids form a thin, two-layer membrane between them. The soft surfaces contain voltage-sensitive channels that allow electrical signals to pass between droplets through the movement of charged atoms or molecules called ions. The membranes store charge, while the channels respond to present and recent signals. When combined, these properties allow the droplets to form a synthetic, interconnected network that the researchers call a “neuromorphic tissue.”

They detailed these tissues’ properties in a paper published in Science Advances, using laboratory measurements and computer simulations to show that interconnecting the droplets’ membranes can significantly improve the material’s ability to process complex and unpredictable signals.

According to Joseph Najem, associate professor of mechanical engineering, associate director of the Convergence Center for Living Multifunctional Materials System and co-corresponding author on the paper, integrating a computer interface or sensor in the body, for example, is not as simple as implanting circuitry because even though both electronics and biology process signals and compute, they carry information differently. Conventional electronics work by moving charge carriers like electrons through conductive materials such as metal wires, while the electrical signaling that happens in the body relies on ions moving through fluids and across cell membranes.

“Scientists and developers must create interfaces in devices that use traditional silicon computing to process and translate between electronic and ionic signals,” said Najem, who holds an additional affiliation in the Intercollege Graduate Degree Program in Materials Science and Engineering at Penn State. “However, these interfaces can delay and distort signals, while adding significant energy costs. To address this problem, we must investigate creating new computing platforms that go beyond just electronics.”

Ionic computing explores approaches to processing information using ions instead of the traditional charge carriers used in electronics. Neuromorphic computing is an approach to computing that draws inspiration from the brain’s structure and information processing functionality. The team’s new approach uses aqueous compartments, lipid membranes and peptide ion channels — structures that mimic the natural architecture of the brain — to bring both the structure and functionality of these ideas together, drawing on the nodes' interconnectivity to create electrical feedback within the material.

With this architecture, the researchers can use reservoir computing, in which a changing input produces a pattern of responses that retains traces of earlier inputs. In this network, membrane charging and ion-channel activity generate responses, and the connections allow the droplets to influence one another. Coupled nodes can accumulate information in the form of electric charge, passing it to the next node in the chain. This behavior, which also occurs in neurons, allows electricity to rapidly transfer through each node without losing overall signal clarity. The membranes between the droplets give the network short-term, fading memory, so its response to a new input depends partly on recent inputs, Najem explained.

“In traditional systems, memory and processing are separated, which consumes a lot of energy to make those actions work together,” Najem said. “In the brain, information processing and memory occur in the same place. That’s why we are seeing interest in this idea of neuromorphic computing, as it could significantly reduce the amount of energy needed for operation.”

Joseph explained that individual droplets can connect to as many as six neighbors in a structure reminiscent of a honeycomb, noting that various arrangements provide different patterns of connectivity. To measure the dynamics of a droplet chain, the team introduced an electrical current, recording voltages at individual nodes. These measurements were compared with a physics-based model of the network, allowing the researchers to explore how connections and differences between droplets influence overall computational performance.

Seven individual nodes can join together in a hexagonal pattern to form a structure, pictured, similar to a honeycomb. Credit: Provided by Ahmed Mohamed. All Rights Reserved.

“When the droplets join together this way, you create coupling that enables rich electrical dynamics capable of processing complex signals that change over time,” Najem explained. “In our simulations, these coupled droplets generated more accurate signal predictions than the uncoupled reservoirs we tested.”

The team assessed computational performance using simulations based on its experimentally tested model, comparing interconnected reservoirs with parallel reservoirs whose node’s responded independently. Using benchmark tests that use mathematical equations to model chaotic behavior, the team found that connected networks reduced prediction error by nearly 50% when compared to independent nodes. By reducing these errors, the team can better understand how electricity will flow in extremely complicated systems.

Although external equipment is still used to supply inputs and measure responses, Najem said, the fact that the network’s recurrent connections arise within the material without external feedback circuitry is what makes it so exciting.

“With silicon, circuitry is needed to build a computer,” Najem said. “With our approach, the circuitry is the material itself: the membranes store charge, and ion channels regulate signals between droplets. This draws inspiration from the way biological membranes contribute to information processing and memory.”

The team plans to continue investigating their material, its properties and how it can be further optimized for ionic computing. According to Najem, potential future applications include adaptive sensors, soft robotics and computing at biological interfaces.

“Using this material, we want to create a framework to enable commercially scalable ionic computing,” Najem said. “Our hope is to one day harness this technology to make Jelly-like computer chips to complement and optimize electronics in the body.”

Additional co-first authors on the work include Ahmed S. Mohamed, a postdoctoral scholar at Penn State, and Nicholas X. Armendarez, a postdoctoral fellow at the University of Texas at Austin who conducted the work at Penn State as a doctoral student. Md Sakib Hasan, an assistant professor at the University of Massachusetts Dartmouth, contributed as a co-corresponding author.

Ty Tkacik(tct5204@psu.edu) Penn State

 

DIGITAL LIFE

Five ways to beat dating algorithms at their own game

You open a dating app intending to spend ten minutes on it. An hour later, you are still swiping. You might have a few new matches, but are you any closer to meeting someone you actually like? Arguably, one of the benefits of dating apps is that they make introductions possible on a scale that would once have been hard to imagine.

They can help people meet outside their usual circles and offer a less daunting way to signal interest. But their design also shapes what we do: profiles arrive one after another, choices take seconds, and there is always the possibility that the next person will be a better match.

In our research, we examined 21 interviews with dating app users alongside public online discussions and French newspaper coverage. We found that people navigate three coexisting, sometimes competing ideas about dating. The first one is the romantic approach to dating where it is important to take time, build trust and look for a connection. The second, the shopping approach, treats dating more like browsing where a person can compare options, filter efficiently and keep their choices open. The third, the systems approach follows the app’s own rhythm: swipe, match, respond to prompts and keep engaging, without necessarily thinking about the long-term goals.

Most people do not fit neatly into just one of these approaches. Someone may hope for a lasting relationship while filtering profiles like a careful shopper. Another may enjoy casual dating but resent feeling pushed to make endless quick decisions. That tension helps explain why using an app can feel both useful and exhausting.

So how can you ‘beat’ a dating algorithm? Despite years of research dedicated to looking for determinants of online dating success, there is no proven secret combination of swipes, photos or messages that will make a platform deliver the right partner.

Ask yourself instead, how can you stop the app’s rhythm from setting the terms of your romantic life? Here’s how:

-Keep a clear picture of your intentions...Before opening the app, consider whether you want a committed relationship, casual dates or simply to meet people without deciding yet. Any of these is a valid aim. Knowing yours can help you choose whom to talk to and what to say. It can also make it easier to notice when you are following someone else’s expectations because it seems simpler than explaining your own. When a conversation starts to matter to you, share what you are looking for and ask what the other person wants, too. You need not settle everything immediately, but you should not have to guess indefinitely.

-Choose when to stop browsing...One participant in our study, Rose, imposed a swiping ban on herself so she could focus on a few people she was genuinely interested in. Her choice did not reveal a flaw in the app’s code. It changed how she used the app. If you find yourself collecting matches without talking to them, try pausing new searches long enough to give existing conversations your attention.

Use filters and profiles as tools, not verdicts...Some people in our study carefully shaped their profiles, used several apps or filtered matches to save time. These tactics can be useful, particularly when they reflect what you genuinely value. But a profile offers only a partial view of a person. A match is a reason to begin a conversation, not evidence that a relationship will work. Critically, a lack of matches is not a measure of your worth. Research shows that low match rates on dating apps may lead to negative mental health outcomes because the apps create a false equivalence between algorithmic compatibility and personal worth.

-Take promising connections beyond the swipe cycle...Participants seeking closeness often wanted more than an exchange of quick reactions. Some moved conversations off the app or made plans to meet, rather than continuing to browse indefinitely. You can do this at a pace that feels comfortable to both people. The point is to give a connection a chance to develop in a setting where the next profile is not constantly competing for attention.

-The importance of psychological sovereignty...One participant in our study, Alan, deleted an app when dating began to feel like a game, though he sometimes returned later. Taking a break need not mean giving up on meeting someone. It can be a way to check whether the app is still helping you pursue what you want. We call this reclaiming psychological sovereignty. Put plainly, it means keeping hold of your ability to decide what matters to you, how much attention you give the app and when you have had enough. You cannot control who appears on your screen or how the platform ranks profiles, but you can decide what you do next.

Dating apps can open doors, but it is important to remember that you are the one who is in control. You get to choose whether to walk through this door, keep looking, or put your phone away, and no algorithm can make this decision for you.

Dating app algorithms are fundamentally built to keep you on the platform rather than help you leave it, since their revenue relies heavily on your active screen time, ad impressions, and premium subscriptions.

To "beat" these algorithms, you can either manipulate the code to boost your profile visibility (playing the technical game) or reclaim your psychological sovereignty to stop the app's rhythm from dictating your love life (playing the human game).

Here are the five most effective ways to beat dating algorithms at their own game:

1. Be strict and selective with your swipes...Swiping right on every single profile out of frustration will severely damage your hidden desirability score (often rooted in the classic "Elo" ranking system).

• How it works: When you swipe right indiscriminately, the algorithm flags you as a bot or a low-effort user and lowers your profile visibility.

• The fix: Only swipe right on people you genuinely want to talk to. High-selectivity tells the system you are a high-value user, which improves the caliber of profiles you are shown.

2. Guard your time and pause new searches...The interface is intentionally designed to present an infinite loop of options, making you believe the "perfect" match is just one more swipe away.

• How it works: Collecting matches without messaging them triggers an algorithmic confirmation loop where you continuously browse but rarely convert actions into real conversations.

• The fix: Set strict limits on your daily screen time. If you have a few active matches, pause your profile discovery or stop swiping entirely until you have explored those existing conversations.

3. Log In daily but reply quickly...Dating apps prioritize active, highly responsive accounts because they want to avoid matching users with dead or abandoned profiles.

• How it works: Apps like Bumble and Tinder reward "permanent presence." If you open the app frequently and reply to messages fast, the algorithm boosts your visibility in other users' active feeds.

• The fix: Spend at least 5 to 10 minutes a day on the app actively engaging. When you get a match, send a quick, thoughtful opener or reply promptly to signal to the algorithm that you are a highly engaged participant.

4. Treat the profile as an introduction, not a verdict...Algorithms try to cluster users based on physical appearance and superficial data tags, which can lock you into a very narrow loop of recommendations.

• How it works: A digital profile only offers a heavily curated, partial snapshot of a human being. Over-filtering based on checklist items creates a false sense of algorithmic compatibility.

• The fix: Use filters loosely. Treat a match simply as a green light to start talking, not evidence that the relationship will work. Move the conversation off the app or schedule a low-stakes, in-person date (like coffee or a walk) reasonably quickly to test real-world chemistry before algorithmic fatigue sets in.

5. Feed the algorithm fresh data (or hit reset)...The data profiles have on you is historical; if your preferences change, the algorithm can take a very long time to adapt.

• How it works: The system rewards dynamic content and penalizes stagnant bios or years-old photos.

• The fix: Regularly refresh your prompts, change your primary lifestyle tags, and upload new, high-quality photos. If the algorithm has completely pigeonholed you into a low-quality tier, the ultimate bypass is to fully delete your account, wait a few days to avoid a shadowban, and start fresh with a clean history

mundophone