Aurelia Butler

Quantum computers hold the promise of being able to quickly solve extremely complex problems that might take the world’s most powerful supercomputer decades to crack.
But achieving that performance involves building a system with millions of interconnected building blocks called qubits. Making and controlling so many qubits in a hardware architecture is an enormous challenge that scientists around the world are striving to meet.
Toward this goal, researchers at MIT and MITRE have demonstrated a scalable, modular hardware platform that integrates thousands of interconnected qubits onto a customized integrated circuit. This “quantum-system-on-chip” (QSoC) architecture enables the researchers to precisely tune and control a dense array of qubits. Multiple chips could be connected using optical networking to create a large-scale quantum communication network.
By tuning qubits across 11 frequency channels, this QSoC architecture allows for a new proposed protocol of “entanglement multiplexing” for large-scale quantum computing.
The team spent years perfecting an intricate process for manufacturing two-dimensional arrays of atom-sized qubit microchiplets and transferring thousands of them onto a carefully prepared complementary metal-oxide semiconductor (CMOS) chip. This transfer can be performed in a single step.
“We will need a large number of qubits, and great control over them, to really leverage the power of a quantum system and make it useful. We are proposing a brand new architecture and a fabrication technology that can support the scalability requirements of a hardware system for a quantum computer,” says Linsen Li, an electrical engineering and computer science (EECS) graduate student and lead author of a paper on this architecture.
Li’s co-authors include Ruonan Han, an associate professor in EECS, leader of the Terahertz Integrated Electronics Group, and member of the Research Laboratory of Electronics (RLE); senior author Dirk Englund, professor of EECS, principal investigator of the Quantum Photonics and Artificial Intelligence Group and of RLE; as well as others at MIT, Cornell University, the Delft Institute of Technology, the Army Research Laboratory, and the MITRE Corporation. The paper appears in Nature.

Diamond microchiplets
While there are many types of qubits, the researchers chose to use diamond color centers because of their scalability advantages. They previously used such qubits to produce integrated quantum chips with photonic circuitry.
Qubits made from diamond color centers are “artificial atoms” that carry quantum information. Because diamond color centers are solid-state systems, the qubit manufacturing is compatible with modern semiconductor fabrication processes. They are also compact and have relatively long coherence times, which refers to the amount of time a qubit’s state remains stable, due to the clean environment provided by the diamond material.
In addition, diamond color centers have photonic interfaces which allows them to be remotely entangled, or connected, with other qubits that aren’t adjacent to them.
“The conventional assumption in the field is that the inhomogeneity of the diamond color center is a drawback compared to identical quantum memory like ions and neutral atoms. However, we turn this challenge into an advantage by embracing the diversity of the artificial atoms: Each atom has its own spectral frequency. This allows us to communicate with individual atoms by voltage tuning them into resonance with a laser, much like tuning the dial on a tiny radio,” says Englund.
This is especially difficult because the researchers must achieve this at a large scale to compensate for the qubit inhomogeneity in a large system.

To communicate across qubits, they need to have multiple such “quantum radios” dialed into the same channel. Achieving this condition becomes near-certain when scaling to thousands of qubits. To this end, the researchers surmounted that challenge by integrating a large array of diamond color center qubits onto a CMOS chip which provides the control dials. The chip can be incorporated with built-in digital logic that rapidly and automatically reconfigures the voltages, enabling the qubits to reach full connectivity.
“This compensates for the in-homogenous nature of the system. With the CMOS platform, we can quickly and dynamically tune all the qubit frequencies,” Li explains.
Lock-and-release fabrication
To build this QSoC, the researchers developed a fabrication process to transfer diamond color center “microchiplets” onto a CMOS backplane at a large scale.
They started by fabricating an array of diamond color center microchiplets from a solid block of diamond. They also designed and fabricated nanoscale optical antennas that enable more efficient collection of the photons emitted by these color center qubits in free space.
Then, they designed and mapped out the chip from the semiconductor foundry. Working in the MIT.nano cleanroom, they post-processed a CMOS chip to add microscale sockets that match up with the diamond microchiplet array.
They built an in-house transfer setup in the lab and applied a lock-and-release process to integrate the two layers by locking the diamond microchiplets into the sockets on the CMOS chip. Since the diamond microchiplets are weakly bonded to the diamond surface, when they release the bulk diamond horizontally, the microchiplets stay in the sockets.
“Because we can control the fabrication of both the diamond and the CMOS chip, we can make a complementary pattern. In this way, we can transfer thousands of diamond chiplets into their corresponding sockets all at the same time,” Li says.
The researchers demonstrated a 500-micron by 500-micron area transfer for an array with 1,024 diamond nanoantennas, but they could use larger diamond arrays and a larger CMOS chip to further scale up the system. In fact, they found that with more qubits, tuning the frequencies actually requires less voltage for this architecture.
“In this case, if you have more qubits, our architecture will work even better,” Li says.
The team tested many nanostructures before they determined the ideal microchiplet array for the lock-and-release process. However, making quantum microchiplets is no easy task, and the process took years to perfect.
“We have iterated and developed the recipe to fabricate these diamond nanostructures in MIT cleanroom, but it is a very complicated process. It took 19 steps of nanofabrication to get the diamond quantum microchiplets, and the steps were not straightforward,” he adds.
Alongside their QSoC, the researchers developed an approach to characterize the system and measure its performance on a large scale. To do this, they built a custom cryo-optical metrology setup.
Using this technique, they demonstrated an entire chip with over 4,000 qubits that could be tuned to the same frequency while maintaining their spin and optical properties. They also built a digital twin simulation that connects the experiment with digitized modeling, which helps them understand the root causes of the observed phenomenon and determine how to efficiently implement the architecture.
In the future, the researchers could boost the performance of their system by refining the materials they used to make qubits or developing more precise control processes. They could also apply this architecture to other solid-state quantum systems. More

It’s every driver’s nightmare: a pedestrian stepping out in front of the car seemingly out of nowhere, leaving only a fraction of a second to brake or steer the wheel and avoid the worst. Some cars now have camera systems that can alert the driver or activate emergency braking. But these systems are not yet fast or reliable enough, and they will need to improve dramatically if they are to be used in autonomous vehicles where there is no human behind the wheel.
Quicker detection using less computational power
Now, Daniel Gehrig and Davide Scaramuzza from the Department of Informatics at the University of Zurich (UZH) have combined a novel bio-inspired camera with AI to develop a system that can detect obstacles around a car much quicker than current systems and using less computational power. The study is published in this week’s issue of Nature.
Most current cameras are frame-based, meaning they take snapshots at regular intervals. Those currently used for driver assistance on cars typically capture 30 to 50 frames per second and an artificial neural network can be trained to recognize objects in their images — pedestrians, bikes, and other cars. “But if something happens during the 20 or 30 milliseconds between two snapshots, the camera may see it too late. The solution would be increasing the frame rate, but that translates into more data that needs to be processed in real-time and more computational power,” says Daniel Gehrig, first author of the paper.
Combining the best of two camera types with AI
Event cameras are a recent innovation based on a different principle. Instead of a constant frame rate, they have smart pixels that record information every time they detect fast movements. “This way, they have no blind spot between frames, which allows them to detect obstacles more quickly. They are also called neuromorphic cameras because they mimic how human eyes perceive images,” says Davide Scaramuzza, head of the Robotics and Perception Group. But they have their own shortcomings: they can miss things that move slowly and their images are not easily converted into the kind of data that is used to train the AI algorithm.
Gehrig and Scaramuzza came up with a hybrid system that combines the best of both worlds: It includes a standard camera that collects 20 images per second, a relatively low frame rate compared to the ones currently in use. Its images are processed by an AI system, called a convolutional neural network, that is trained to recognize cars or pedestrians. The data from the event camera is coupled to a different type of AI system, called an asynchronous graph neural network, which is particularly apt for analyzing 3-D data that change over time. Detections from the event camera are used to anticipate detections by the standard camera and also boost its performance. “The result is a visual detector that can detect objects just as quickly as a standard camera taking 5,000 images per second would do but requires the same bandwidth as a standard 50-frame-per-second camera,” says Daniel Gehrig.
One hundred times faster detections using less data
The team tested their system against the best cameras and visual algorithms currently on the automotive market, finding that it leads to one hundred times faster detections while reducing the amount of data that must be transmitted between the camera and the onboard computer as well as the computational power needed to process the images without affecting accuracy. Crucially, the system can effectively detect cars and pedestrians that enter the field of view between two subsequent frames of the standard camera, providing additional safety for both the driver and traffic participants — which can make a huge difference, especially at high speeds.
According to the scientists, the method could be made even more powerful in the future by integrating cameras with LiDAR sensors, like the ones used on self-driving cars. “Hybrid systems like this could be crucial to allow autonomous driving, guaranteeing safety without leading to a substantial growth of data and computational power,” says Davide Scaramuzza. More

Researchers at Duke University have developed an assistive machine learning model that greatly improves the ability of medical professionals to read the electroencephalography (EEG) charts of intensive care patients.
Because EEG readings are the only method for knowing when unconscious patients are in danger of suffering a seizure or are having seizure-like events, the computational tool could help save thousands of lives each year. The results appear online May 23 in the New England Journal of Medicine AI.
EEGs use small sensors attached to the scalp to measure the brain’s electrical signals, producing a long line of up and down squiggles. When a patient is having a seizure, these lines jump up and down dramatically like a seismograph during an earthquake — a signal that is easy to recognize. But other medically important anomalies called seizure-like events are much more difficult to discern.
“The brain activity we’re looking at exists along a continuum, where seizures are at one end, but there’s still a lot of events in the middle that can also cause harm and require medication,” said Dr. Brandon Westover, associate professor of neurology at Massachusetts General Hospital and Harvard Medical School. “The EEG patterns caused by those events are more difficult to recognize and categorize confidently, even by highly trained neurologists, which not every medical facility has. But doing so is extremely important to the health outcomes of these patients.”
To build a tool to help make these determinations, the doctors turned to the laboratory of Cynthia Rudin, the Earl D. McLean, Jr. Professor of Computer Science and Electrical and Computer Engineering at Duke. Rudin and her colleagues specialize in developing “interpretable” machine learning algorithms. While most machine learning models are a “black box” that makes it impossible for a human to know how it’s reaching conclusions, interpretable machine learning models essentially must show their work.
The research group started by gathering EEG samples from over 2,700 patients and having more than 120 experts pick out the relevant features in the graphs, categorizing them as either a seizure, one of four types of seizure-like events or ‘other.’ Each type of event appears in EEG charts as certain shapes or repetitions in the undulating lines. But because these charts are rarely steadfast in their appearance, telltale signals can be interrupted by bad data or can mix together to create a confusing chart.
“There is a ground truth, but it’s difficult to read,” said Stark Guo, a Ph.D. student working in Rudin’s lab. “The inherent ambiguity in many of these charts meant we had to train the model to place its decisions within a continuum rather than well-defined separate bins.”
When displayed visually, that continuum looks something like a multicolored starfish swimming away from a predator. Each differently colored arm represents one type of seizure-like event the EEG could represent. The closer the algorithm puts a specific chart toward the tip of an arm, the surer it is of its decision, while those placed closer to the central body are less certain.

Besides this visual classification, the algorithm also points to the patterns in the brainwaves that it used to make its determination and provides three examples of professionally diagnosed charts that it sees as being similar.
“This lets a medical professional quickly look at the important sections and either agree that the patterns are there or decide that the algorithm is off the mark,” said Alina Barnett, a postdoctoral research associate in the Rudin lab. “Even if they’re not highly trained to read EEGs, they can make a much more educated decision.”
Putting the algorithm to the test, the collaborative team had eight medical professionals with relevant experience categorize 100 EEG samples into the six categories, once with the help of AI and once without. The performance of all of the participants greatly improved, with their overall accuracy rising from 47% to 71%. Their performance also rose above those using a similar “black box” algorithm in a previous study.
“Usually, people think that black box machine learning models are more accurate, but for many important applications, like this one, it’s just not true,” said Rudin. “It’s much easier to troubleshoot models when they are interpretable. And in this case, the interpretable model was actually more accurate. It also provides a bird’s eye view of the types of anomalous electrical signals that occur in the brain, which is really useful for care of critically ill patients.”
This work was supported by the National Science Foundation (IIS-2147061, HRD-2222336, IIS-2130250, 2014431), the National Institutes of Health (R01NS102190, R01NS102574, R01NS107291, RF1AG064312, RF1NS120947, R01AG073410, R01HL161253, K23NS124656, P20GM130447) and the DHHS LB606 Nebraska Stem Cell Grant. More

Cambridge researchers have shown that members of the public have little trouble in learning very quickly how to use a third thumb — a controllable, prosthetic extra thumb — to pick up and manipulate objects.
The team tested the robotic device on a diverse range of participants, which they say is essential for ensuring new technologies are inclusive and can work for everyone.
An emerging area of future technology is motor augmentation — using motorised wearable devices such as exoskeletons or extra robotic body parts to advance our motor capabilities beyond current biological limitations.
While such devices could improve the quality of life for healthy individuals who want to enhance their productivity, the same technologies can also provide people with disabilities new ways to interact with their environment.
Professor Tamar Makin from the Medical Research Council (MRC) Cognition and Brain Sciences Unit at the University of Cambridge said: “Technology is changing our very definition of what it means to be human, with machines increasingly becoming a part of our everyday lives, and even our minds and bodies.
“These technologies open up exciting new opportunities that can benefit society, but it’s vital that we consider how they can help all people equally, especially marginalised communities who are often excluded from innovation research and development. To ensure everyone will have the opportunity to participate and benefit from these exciting advances, we need to explicitly integrate and measure inclusivity during the earliest possible stages of the research and development process.”
Dani Clode, a collaborator within Professor Makin’s lab, has developed the Third Thumb, an extra robotic thumb aimed at increasing the wearer’s range of movement, enhancing their grasping capability and expanding the carrying capacity of the hand. This allows the user to perform tasks that might be otherwise challenging or impossible to complete with one hand or to perform complex multi-handed tasks without having to coordinate with other people.

The Third Thumb is worn on the opposite side of the palm to the biological thumb and controlled by a pressure sensor placed under each big toe or foot. Pressure from the right toe pulls the Thumb across the hand, while the pressure exerted with the left toe pulls the Thumb up toward the fingers. The extent of the Thumb’s movement is proportional to the pressure applied, and releasing pressure moves it back to its original position.
In 2022, the team had the opportunity to test the Third Thumb at the annual Royal Society Summer Science Exhibition, where members of the public of all ages were able to use the device during different tasks. The results are published today in Science Robotics.
Over the course of five days, the team tested 596 participants, ranging in age from three to 96 years old and from a wide range of demographic backgrounds. Of these, only four were unable to use the Third Thumb, either because it did not fit their hand securely, or because they were unable to control it with their feet (the pressure sensors developed specifically for the exhibition were not suitable for very lightweight children).
Participants were given up to a minute to familiarise themselves with the device, during which time the team explained how to perform one of two tasks.
The first task involved picking up pegs from a pegboard one at a time with just the Third Thumb and placing them in a basket. Participants were asked to move as many pegs as possible in 60 seconds. 333 participants completed this task.
The second task involved using the Third Thumb together with the wearer’s biological hand to manipulate and move five or six different foam objects. The objects were of various shapes that required different manipulations to be used, increasing the dexterity of the task. Again, participants were asked to move as many objects as they could into the basket within a maximum of 60 seconds. 246 participants completed this task.

Almost everyone was able to use the device straightaway. 98% of participants were able to successfully manipulate objects using the Third Thumb during the first minute of use, with only 13 participants unable to perform the task.
Ability levels between participants were varied, but there were no differences in performance between genders, nor did handedness change performance — despite the Thumb always being worn on the right hand. There was no definitive evidence that people who might be considered ‘good with their hands’ — for example, they were learning to play a musical instrument, or their jobs involved manual dexterity — were any better at the tasks.
Older and younger adults had a similar level of ability when using the new technology, though further investigation just within the older adults age bracket revealed a decline in performance with increasing age. The researchers say this effect could be due to the general degradation in sensorimotor and cognitive abilities that are associated with ageing and may also reflect a generational relationship to technology.
Performance was generally poorer among younger children. Six out of the 13 participants that could not complete the task were below the age of 10 years old, and of those that did complete the task, the youngest children tended to perform worse compared to older children. But even older children (aged 12-16 years) struggled more than young adults.
Dani said: “Augmentation is about designing a new relationship with technology — creating something that extends beyond being merely a tool to becoming an extension of the body itself. Given the diversity of bodies, it’s crucial that the design stage of wearable technology is as inclusive as possible. It’s equally important that these devices are accessible and functional for a wide range of users. Additionally, they should be easy for people to learn and use quickly.”
Co-author Lucy Dowdall, also from the MRC Cognition and Brain Science Unit, added: “If motor augmentation — and even broader human-machine interactions — are to be successful, they’ll need to integrate seamlessly with the user’s motor and cognitive abilities. We’ll need to factor in different ages, genders, weight, lifestyles, disabilities — as well as people’s cultural, financial backgrounds, and even likes or dislikes of technology. Physical testing of large and diverse groups of individuals is essential to achieve this goal.”
There are countless examples of where a lack of inclusive design considerations has led to technological failure: Automated speech recognition systems that convert spoken language to text have been shown to perform better listening to white voices over Black voices. Some augmented reality technologies have been found to be less effective for users with darker skin tones. Women face a higher health risk from car accidents, due to car seats and seatbelts being primarily designed to accommodate ‘average’ male-sized dummies during crash testing. Hazardous power and industrial tools designed for a right-hand dominant use or grip have resulted in more accidents when operated by left-handers forced to use their non-dominant hand.This research was funded by the European Research Council, Wellcome, the Medical Research Council and Engineering and Physical Sciences Research Council. More

Researchers from the Cluster of Excellence Collective Behaviour developed a computer vision framework for posture estimation and identity tracking which they can use in indoor environments as well as in the wild. They have thus taken an important step towards markerless tracking of animals in the wild using computer vision and machine learning.
Two pigeons are pecking grains in a park in Konstanz. A third pigeon flies in. There are four cameras in the immediate vicinity. Doctoral students Alex Chan and Urs Waldmann from the Cluster of Excellence Collective Behaviour at the University of Konstanz are filming the scene. After an hour, they return with the footage to their office to analyze it with a computer vision framework for posture estimation and identity tracking. The framework detects and draws a box around all pigeons. It records central body parts and determines their posture, their position, and their interaction with the other pigeons around them. All of this happened without any markers being attached to pigeons or any need for human being called in to help. This would not have been possible just a few years ago.
3D-MuPPET, a framework to estimate and track 3D poses of up to 10 pigeons
Markerless methods for animal posture tracking have been rapidly developed recently, but frameworks and benchmarks for tracking large animal groups in 3D are still lacking. To overcome this gap, researchers from the Cluster of Excellence Collective Behaviour at the University of Konstanz and the Max Planck Institute of Animal Behavior present 3D-MuPPET, a framework to estimate and track 3D poses of up to 10 pigeons at interactive speed using multiple camera views. The related publication was recently published in the International Journal of Computer Vision (IJCV).
Important milestone in animal posture tracking and automatic behavioural analysis
Urs Waldmann and Alex Chan recently finalized a new method, called 3D-MuPPET, which stands for 3D Multi-Pigeon Pose Estimation and Tracking. 3D-MuPPET is a computer vision framework for posture estimation and identity tracking for up to 10 individual pigeons from 4 camera views, based on data collected both in captive environments and even in the wild. “We trained a 2D keypoint detector and triangulated points into 3D, and also show that models trained on single pigeon data work well with multi-pigeon data,” explains Urs Waldmann. This is a first example of 3D animal posture tracking for an entire group of up to 10 individuals. Thus, the new framework provides a concrete method for biologists to create experiments and measure animal posture for automatic behavioural analysis. “This framework is an important milestone in animal posture tracking and automatic behavioural analysis,” as Alex Chan and Urs Waldmann say.
Framework can be used in the wild
In addition to tracking pigeons indoors, the framework is also extended to pigeons in the wild. “Using a model that can identify the outline of any object in an image called the Segment Anything Model, we further trained a 2D keypoint detector with a masked pigeon from the captive data, then applied the model to pigeon videos outdoors without any extra model finetuning,” states Alex Chan. 3D-MuPPET presents one of the first case-studies on how to transition from tracking animals in captivity towards tracking animals in the wild, allowing fine-scaled behaviours of animals to be measured in their natural habitats. The developed methods can potentially be applied across other species in future work, with potential application for large scale collective behaviour research and species monitoring in a non-invasive way.
3D-MuPPET showcases a powerful and flexible framework for researchers who would like to use 3D posture reconstruction for multiple individuals to study collective behaviour in any environments or species. As long as a multi-camera setup and a 2D posture estimator is available, the framework can be applied to track 3D postures of any animals. More

With generative artificial intelligence (GenAI) transforming the social interaction landscape in recent years, large language models (LLMs), which use deep-learning algorithms to train GenAI platforms to process language, have been put in the spotlight. A recent study by The Hong Kong Polytechnic University (PolyU) found that LLMs perform more like the human brain when being trained in more similar ways as humans process language, which has brought important insights to brain studies and the development of AI models.
Current large language models (LLMs) mostly rely on a single type of pretraining — contextual word prediction. This simple learning strategy has achieved surprising success when combined with massive training data and model parameters, as shown by popular LLMs such as ChatGPT. Recent studies also suggest that word prediction in LLMs can serve as a plausible model for how humans process language. However, humans do not simply predict the next word but also integrate high-level information in natural language comprehension.
A research team led by Prof. LI Ping, Dean of the Faculty of Humanities and Sin Wai Kin Foundation Professor in Humanities and Technology at PolyU, has investigated the next sentence prediction (NSP) task, which simulates one central process of discourse-level comprehension in the human brain to evaluate if a pair of sentences is coherent, into model pretraining and examined the correlation between the model’s data and brain activation. The study has been recently published in the academic journal Sciences Advances.
The research team trained two models, one with NSP enhancement and the other without, both also learned word prediction. Functional magnetic resonance imaging (fMRI) data were collected from people reading connected sentences or disconnected sentences. The research team examined how closely the patterns from each model matched up with the brain patterns from the fMRI brain data.
It was clear that training with NSP provided benefits. The model with NSP matched human brain activity in multiple areas much better than the model trained only on word prediction. Its mechanism also nicely maps onto established neural models of human discourse comprehension. The results gave new insights into how our brains process full discourse such as conversations. For example, parts of the right side of the brain, not just the left, helped understand longer discourse. The model trained with NSP could also better predict how fast someone read — showing that simulating discourse comprehension through NSP helped AI understand humans better.
Recent LLMs, including ChatGPT, have relied on vastly increasing the training data and model size to achieve better performance. Prof. Li Ping said, “There are limitations in just relying on such scaling. Advances should also be aimed at making the models more efficient, relying on less rather than more data. Our findings suggest that diverse learning tasks such as NSP can improve LLMs to be more human-like and potentially closer to human intelligence.”
He added, “More importantly, the findings show how neurocognitive researchers can leverage LLMs to study higher-level language mechanisms of our brain. They also promote interaction and collaboration between researchers in the fields of AI and neurocognition, which will lead to future studies on AI-informed brain studies as well as brain-inspired AI.” More

A study by researchers from Nanyang Technological University, Singapore (NTU Singapore) has found that Singaporeans are open to ride air taxis, which are small autonomous aircraft that carry passengers over short distances. Through a study of 1,002 participants, the NTU Singapore team found that almost half (45.7 per cent) say they intend to use this mode of transport when it becomes available, with over one-third (36.2 per cent) planning to do so regularly.
According to the findings published online in the journal Technology in Society in April, the intention to take autonomous air taxis is associated with factors such as trust in the AI technology deployed in air taxis, hedonic motivation (the fun or pleasure derived from using technology), performance expectancy (the degree to which users expect that using the system will benefit them), and news media attention (the amount of attention paid to news about air taxis).
Air taxis and autonomous drone services are close to becoming a reality: China’s aviation authority issued its first safety approval certification last year to a Chinese drone maker for trial operations, and in Europe, authorities are working to certify air taxis safe to serve passengers at the Paris Olympics this year.
For Singapore, which is looking to become a base for air taxi companies1, the study findings could help the sector achieve lift-off, said the research team from NTU’s Wee Kim Wee School of Communication and Information (WKWSCI) led by Professor Shirley Ho. Professor Ho, who is also NTU’s Associate Vice President for Humanities, Social Sciences & Research Communication, said: “Even though air taxis have yet to be deployed in Singapore, close to half of those surveyed said they would be keen to take one. Singapore aims to become base for air taxi firms[1]; CAAS working with regional counterparts on guidelines, CNA 2 air taxis in the future. This signifies a positive step forward for a nascent technology. Our study represents a significant step forward in understanding the factors that influence one’s intention to take air taxis. Insights into the public perception of air taxis will enable policymakers and tech developers to design targeted interventions that encourage air taxi use as they look to build up an air taxi industry in Singapore.”
The study aligns with NTU’s goal of pursuing research aligned with national priorities and with the potential for significant intellectual and societal impact, as articulated in the NTU 2025 five-year strategic plan.
How the study was conducted
To gauge the public perception of air taxis, the NTU WKWSCI team surveyed 1,002 Singaporeans and permanent residents, drawing on a validated model[2] that measures technology acceptance and use and the factors driving this behaviour.

Participants were asked to score on a five-point scale in response to various statements about factors such as their trust in the AI system used in air taxis, their attention to news reports on air taxis, their perceived ease of use and usefulness of air taxis, as well as their attitudes and intention to take air taxis in the future.
The scores for each participant were then tabulated and used in statistical analyses to find out how these factors related to the participant’s intention to take air taxis.
“Generally positive” sentiment about air taxis
Upon tabulating the scores, the researchers found that sentiments around air taxis are generally positive among the participants. Almost half (45.7 per cent) said they intend to use this mode of transport when it becomes available. Close to four in 10 (36.2 per cent) said they plan to do so regularly.
Close to six in 10 (57 per cent) thought taking air taxis would be fun, and 53 per cent said they were excited about taking air taxis.
Six in 10 (60.9 per cent) agreed that taking air taxis would help to get things done more quickly, and 61.2 per cent believed that it would increase productivity.

Half the participants also trusted the competency of the AI technology used in air taxis, and the AI engineers building the technology. Five in 10 (52.9 per cent) agreed that the AI system in air taxis would be competent and effective at helping to transport people.
Factors that predict air taxi use
Upon conducting statistical analyses on the survey data, the researchers found that the following factors directly impacted participants’ intention to take air taxis: news media attention; trust in the AI system used in air taxis; attitude towards air taxis; performance expectancy; hedonic motivation; price value; social influence; habit (the perception that taking air taxis could become a habit).These findings suggest that when Singaporeans consider whether they would use autonomous air taxis, not only do they value the practical aspects of the technology, but also how much they can trust the AI system, said NTU WKWSCI’s PhD student Justin Cheung, a co-author of the study.
Surprisingly, habit was the most robust predictor of people’s intention to use air taxis, despite the relatively smaller number of participants who agreed that taking the vehicles would become a habit for them, he said. This suggests that while the user base for autonomous passenger drones may be small, it could be a loyal one, he added.
Another robust predictor of use intention was attention to news media. In addition, the researchers found that news media attention could shape intentions to use air taxis and attitudes towards them by influencing trust in the AI systems, as well as the engineers who develop the AI systems behind air taxis.
Prof Ho said: “When technologies are yet to be deployed in the public sphere, news media offers the main and, in many instances, the only source of information for members of the public. Our findings suggest that policymakers could leverage positive news media reporting when introducing air taxis to shape public perceptions and thereby use intention.”
Credibility affects trust in media reports on AI technology
These findings build on a study authored by Prof Ho and WKWSCI research fellow Goh Tong Jee. Published online in journal Science Communication in May, the study identified considerations that could affect the public’s trust in media organisations, policymakers and tech developers that introduce AI in autonomous vehicles (AVs).
Through six focus group discussions with 56 drivers and non-drivers, the researchers found that media credibility is a foundation upon which the public would evaluate the trustworthiness of media organisations.
The focus group discussion participants said they would consider qualities such as balance, comprehensiveness, persuasiveness and objectivity of media organisations when assessing their ability to create quality content.
The researchers also found that non-drivers raised more qualities than drivers regarding trust in media organisations. The researchers attributed this observation to the enthusiasm non-drivers could have over the prospective use of AVs, which drove the non-drivers’ tendency to seek information.
Some qualities raised only by non-drivers during the focus group discussions include a media organisation’s ability to spur discussions on whether AV is a need or a want. Another consideration is a media organisation’s ability to create varied content.
Non-drivers also shared their expectations that media organisations should be transparent and reveal “unflattering” information in the public’s interest during crises, even if it means affecting the reputation of policymakers or tech developers.
The findings from these two studies reaffirm the need for accurate and balanced reporting on AVs such as air taxis, due to the role news media can play in shaping public perception, and the public’s expectations of media organisations, said Prof Ho.
Prof Ho added: “The two studies highlight the importance for media organisations to translate emerging scientific evidence accurately to facilitate informed decision-making. Given the speed at which innovative technologies emerge in the age of digitalisation, accurate science communication has never been more crucial.”
Notes;
[1]Singapore aims to become base for air taxi firms; CAAS working with regional counterparts on guidelines, CNA
[2] This model, called the Unified Theory of Acceptance and Use of Technology 2, is a validated technology acceptance model that aims to explain user intentions to use an information system and subsequent usage behaviour. More

Imagine if your dead laptop or phone could charge in a minute or if an electric car could be fully powered in 10 minutes.
While not possible yet, new research by a team of CU Boulder scientists could potentially lead to such advances.
Published today in the Proceedings of the National Academy of Sciences, researchers in Ankur Gupta’s lab discovered how tiny charged particles, called ions, move within a complex network of minuscule pores. The breakthrough could lead to the development of more efficient energy storage devices, such as supercapacitors, said Gupta, an assistant professor of chemical and biological engineering.
“Given the critical role of energy in the future of the planet, I felt inspired to apply my chemical engineering knowledge to advancing energy storage devices,” Gupta said. “It felt like the topic was somewhat underexplored and as such, the perfect opportunity.”
Gupta explained that several chemical engineering techniques are used to study flow in porous materials such as oil reservoirs and water filtration, but they have not been fully utilized in some energy storage systems.
The discovery is significant not only for storing energy in vehicles and electronic devices but also for power grids, where fluctuating energy demand requires efficient storage to avoid waste during periods of low demand and to ensure rapid supply during high demand.
Supercapacitors, energy storage devices that rely on ion accumulation in their pores, have rapid charging times and longer life spans compared to batteries.

“The primary appeal of supercapacitors lies in their speed,” Gupta said. “So how can we make their charging and release of energy faster? By the more efficient movement of ions.”
Their findings modify Kirchhoff’s law, which has governed current flow in electrical circuits since 1845 and is a staple in high school students’ science classes. Unlike electrons, ions move due to both electric fields and diffusion, and the researchers determined that their movements at pore intersections are different from what was described in Kirchhoff’s law.
Prior to the study, ion movements were only described in the literature in one straight pore. Through this research, ion movement in a complex network of thousands of interconnected pores can be simulated and predicted in a few minutes.
“That’s the leap of the work,” Gupta said. “We found the missing link.” More