Tech
Urban digital twins – missing pieces and emerging divides | Computer Weekly
Digital twins – virtual representations of environments and dynamics of interest – can address a wide range of decision-making needs and opportunities, and expectations for the technology and related applications are high.
A study from Fortune Business Insights projects the market to grow from $24bn in 2025 to more than $250bn in 2032. Digital twins can support research, planning and operations across a wide range of application areas, such as biological systems, machines and infrastructures, industrial operations, communities and cities, and even simulations of global and geopolitical dynamics. Recent discussions have centred on their use for robotics and robotics management.
The versatility of digital twins is substantial, but hurdles exist that prevent them from reaching their full potential. Some dynamics are not fully captured, while other dynamics are difficult to address comprehensively. In some cases, artificial intelligence (AI) can reduce existing limitations, but use of AI can create its own problematic issues.
Injecting human behaviour
Machines and equipment can be modelled according to physical formulas and accumulated sensor data that capture real-time and real-world behaviour. The same is true for electricity and water networks, for example. These systems are complicated but can be modelled in theory.
Complicated systems behave in predictable ways. Complex systems, in contrast, will behave differently each time, in part because of human behaviour that can change according to many influences. Most digital twins tend to omit human behaviour, while others treat human behaviour as predictable placeholders – in a way, they mechanise human behaviour. But human behaviour and interactions are of crucial importance in simulating dynamics for digital twins for cities and urban environments – after all, that’s what cities and communities are ultimately created for.
Farzin Lotfi-Jam, assistant professor at Cornell University’s College of Architecture, studies the use of technologies to govern cities. He is the director of Cornell University’s Realtime Urbanism Lab, which “investigates the impacts of new technologies that virtualise cities and populations”.
He says: “The global proliferation of urban digital twin models compels a research agenda that investigates the intertwined social, political and technical dimensions of their development, from design to use in planning and governance. In each of these digital twinning concepts is a concept of what a city is. I noticed, looking at all of these, that there’s no people anywhere in any of these concepts.”
A research field far removed from Lotfi-Jam interests could potentially add guidance in populating digital twins for cities. Tianyi Peng, assistant professor in the decision, risk and operations division at Columbia Business School, is looking at the use of AI for decision making. His research looks at what can be used to generate AI agents that mimic human behaviour, such as that in the context of market decisions like shopping preferences and reactions to product stimuli.
The current use of digital twins for urban environments is limited for the lack of realistic representations of humans and their actions and interactions. It is easy to see how the study of individual behaviour and the simulation of group interactions will find use in city digital twins over time.
Peng’s colleague Olivier Toubia, professor of business at Columbia Business School, who is investigating interactions between AI-generated behaviour and how these interactions affect collective behaviour, “combines methods from social sciences and data science to study human processes such as motivation, choice, and creativity”.
Meanwhile, Lydia Chilton, associate professor of computer science at Columbia University School of Engineering & Applied Sciences, is contributing research into how AI agents in simulated environments can mimic unique behaviours of human interactions that can be unpredictable.
Providing comprehensive data
Mutualistic technologies offer ways looks at the wider set of technologies that interact with each other with impact on digital twins and robotics. The emerging network of mutualistic technologies features AI and sensors as the glue that creates positive feedback loops between these technologies. Data is needed to create realistic representations and relevant interactions between virtual and real world. Many times, real-world data can be difficult or expensive to extract though. Then synthetic data can find use. Synesthetic data can come from simulations in digital twins or from AI-based applications.
Commercial relevance of capturing comprehensive data is substantial, particularly for digital twins for urban environments where data from many interdependent networks require inclusion to realistically mirror activities and interactions of systems in cities. Road networks affect traffic patterns and public transportation impacts how people move through cities and therefore where businesses spring up.
Electricity networks, gas distribution, water and sewage lines crisscross urban maps and affect what neighbourhoods might lose power first during outages or which areas are prone to flooding. And flooding can affect power outages, which then can affect public transportation’s reliability, and so on. A very comprehensive view of urban activities is required to visualise interdependencies.
One of the general hurdles to effective and efficient city management are the silos in which urban networks and services operate. Data cannot easily connect; platform and format issues prevent seamless interfacing between systems, thereby posing genuine hurdles to all-encompassing digital twins that can truly capture and reflect the operational, commercial and social ongoings within cities. Therefore, a first step to creating genuinely beneficial urban digital twins often is a rather mundane, administrative step. Collaboration between administrations and agencies is key and the need for compatible data is crucial.
The city of San Diego’s managers realised the importance of such collaboration and created a partnership between the San Diego Association of Governments; the San Diego Regional Economic Development Corporation; San Diego State University; University of California, San Diego; and industrial partners. Interconnected dynamics and challenges in urban environments require connected, relatable data and digital twins that can represent resulting complexities – the collaboration of city administrations and network users is the first step.
Cautioning against developing communal divides
Digital twins will transform the way we plan, design, operate and maintain equipment, networks and urban environments. AI will accelerate their development, improve their performance and enhance their usability. But on the road to ubiquitous use, hurdles and issues need overcoming – some considerations generally associated with virtualisation technologies and use, others relate to AI, which currently is experiencing almost unchecked excitement and investment.
The Brookings Institution recently highlighted the emerging industrial and geographic unevenness of implementing and leveraging AI. The diffusion of AI will occur on different timelines in various industrial sectors. Spending on AI will depend on productivity and economic growth that companies and industries will expect or experience. Available investment capital and shareholder agreement will also play a role.
While it is natural that technology-related companies and finance, logistics and manufacturers firms already foresee substantial changes to their operations, agriculture, mining, personal services (including some aspects of healthcare) and many government services will see less immediate application opportunities. AI’s use for digital twins of cities will therefore initially create uneven representation in various sectors of urban planning and management.
Existing disparities between countries and regions will create geographical unevenness in the use of AI, and therefore in the adoption and diffusion of AI-empowered digital twins. Research by the Brookings Institution states: “Artificial intelligence is transforming the US economy, yet regional disparities in talent development, research capacity and enterprise adoption are stark and not yet fully understood.”
The digital divide emerged as a major concern at the end of the 1990s. Although the effects did not pan out as dramatic as some observers initially warned, the Covid pandemic from 2020 and following years highlighted unevenness in the way individuals, regions and entire countries were able to move personal and commercial activities online. Geographical laggards could develop in which AI implementation is slow, leaving other regions to charge ahead.
The report continues: “Such gaps and deficits may result in unrealised opportunities for productivity growth across disparate industries, and limit discovery and dissemination of the full range of AI use cases. For that matter, disparities in AI readiness may leave some communities to fall behind or slump into ‘development traps’. Imbalances in AI talent, innovation infrastructure and business adoption very well could decide which people and places will prosper in the future – and which will not.”
Internationally, such gaps can lead to “geo-algorithmic inequality”. Digital twins that replicate commercial activities, urban environments, entire ecosystems and eventually even economies as a whole will support the development of climate-resilience strategies, affect investment flows and establish the foundation for regional development plans in developed countries and metropolises.
“By contrast, much of Africa, the Caribbean and parts of Southeast Asia remain invisible in major digital twin ecosystems,” says the report. Data availability is spotty, often non-existent. Therefore, “decisions around infrastructure aid, disaster prevention or carbon offsetting are made with incomplete information – or without them in mind at all”.
Thinking globally, acting locally
The impact on these regions can be dramatic. Geo-algorithmic inequality results in “the uneven inclusion of countries and communities in the simulations that shape global policy, investment, and resilience planning”, according to Brookings Institution.
The effect can cascade towards digital twins that attempt to simulate the global ecosystem. In such virtualisations of the entire planet, structural bias can encode misrepresentations in digital twins and therefore distort resulting applications.
Potential approaches to alleviate such concerns exist. The Gaia-X initiative is looking to establish digital sovereignty by establishing “an ecosystem, whereby data is shared and made available in a trustworthy environment”. And the World Avatar effort is working on an “ecosystem of tools and services that can be used to create an individual digital twin, or network of connected digital twins, to provide a platform of data and model interoperability”.
Data silos of networks or country initiatives can then easily connect to each other. Although laudable, a concerted policy framework is needed to create incentives for corporations and organisations to buy into and fully embrace such efforts.
Martin Schwirn is the author of Small data, big disruptions: How to spot signals of change and manage uncertainty (ISBN 9781632651921) on foresight and horizon scanning. He is a strategy and innovation consultant for Global 2000 companies.
I’ve used TurboTax to file my taxes for several years. It’s the most popular DIY tax service, and also often the cheapest and arguably most straightforward. TurboTax has the filer in mind by utilizing an easy-to-use interface, offering available expert help, with different options for document auto-upload; helpful tips and information regarding tax requirements; and transparent, low-cost options for every type of filer.
The service makes it super easy for returning users by storing previous years’ information, allowing easy auto-upload, and remembering choices and previously used forms from years past. Doing my taxes as a returning user with TurboTax takes a fraction of the time of other tax services I have tested. (Need a jumping off point? I’ve got a guide on how to file your taxes online for extra help.)
Yes, You Can Actually File for Free
If you haven’t tried TurboTax, this is the best time to see if it’s the right fit for you (and be able to file for free). You can file both state and federal taxes for $0 right now. There are only a few requirements for this awesome free filing deal. You must not have filed with TurboTax before (and are switching from another provider), and you must file in the TurboTax mobile app by February 28. You’ll need to both start and file within the mobile app; this is only eligible on DIY (self-guided) tax services and excludes expert assist products. This means that it applies to Simple Form 1040 returns only (meaning no schedules, except for EITC, CTC, student loan interest, and Schedule 1-A forms are eligible).
One of the downsides to TurboTax is that while it’s (in my opinion) the easiest-to-use interface with seamless auto-upload features, it can be a bit more expensive than similar competitors. I’ve used FreeTaxUSA in the past, when my income was lower and my taxes were simpler. The service is very similar in design to TurboTax, and while it is still a low-cost option, it’s not completely 100 percent free, as it charge $16 for filing a state return. Plus, when I tested the service last year, FreeTaxUSA gave me the highest amount of taxes owed from all services I tested.
TurboTax filed more than twice the number of free returns as FreeTaxUSA last year (based on the total number of federal and state returns filed in Tax Year 2024). And this tax season, more than 100 million people in the US are eligible for free filing with TurboTax. If you file your own federal and state returns using DIY TurboTax products, filing will be free if you use the mobile app until February 28.
Filing in Your Hands
Filing taxes can be confusing and potentially expensive. While I urge anyone who hasn’t filed with TurboTax to take advantage of the free federal and state filing deal through the mobile app, there are several options if you have filed with the service before or have more complicated returns that may require additional assistance.
There are three options for filers: DIY, where you file yourself with step-by-step instructions (the previously mentioned service eligible for the free filing deal); Expert Assist, where you get help from tax experts throughout the process and have the expert review it before submitting; or you can also get your taxes done completely by a local tax expert with Expert Full Service. Prices vary based on the chosen tier and when you file (the earlier, the cheaper, especially if you’re able to file before March).
The filing process starts out with a helpful questionnaire so that the program knows which sections are applicable to you, like dependents, assets, and education, so you’re not slogging through things that aren’t relevant. At the beginning, TurboTax also estimated the time it’d take to finish and asked how I filed last year—no other service I previously tested did either, which was helpful in estimating how long the process would take.
Other MagSafe Power Banks to Consider
We like a few other MagSafe power banks that didn’t make it into our top picks.
Apple’s MagSafe Battery for iPhone Air for $99: The super svelte iPhone Air doesn’t have room for a big battery, so Apple offers this perfectly sized MagSafe add-on, capable of charging wirelessly at 12 watts. But, with just 3,149 mAh of power (it charged the iPhone Air to 68 percent), it’s awfully pricey. Still, it’s one of the few perfectly designed for the iPhone Air. You can technically use it with other iPhones, but you’ll have to rotate the power bank so that it hangs horizontally.
Statik State Power Bank for $60: This pack uses semisolid battery tech, meaning there’s less liquid inside, so it’s safer (won’t catch fire, even if damaged), and it should last longer. Statik suggests double the lifespan. It certainly keeps its cool, offering 5,000 mAh at up to 15 watts or 20-watt USB-C charging. I like it, but the similar Kuxiu power bank recommended above is slightly more compact and cheaper.
Ecoflow Rapid Qi2 Power Bank for $90: Slim and speedy, this power bank is an impressive gadget for a company we usually associate with portable power stations. It is Qi2 certified for up to 15-watt wireless charging, but there’s also a USB-C port that can deliver up to 36 watts, and it supports a bunch of charging protocols (PD 3.0, PPS, and QC 3.0). To sweeten the deal further, it has a wee kickstand.
Photograph: Simon Hill
Anker Nano Power Bank for $55: Anker has almost managed to match the slimmest power bank above with its new Nano Qi2 power bank, measuring just 0.34 inches thick. It keeps its cool, charges at up to 15 watts, and fills most compatible phones to just over the 50-percent mark. If you want a slim Qi2-certified power bank, pick this.
Mous MagSafe Compatible Wireless Power Bank for $40: I don’t have any major complaints about this MagSafe power bank. The 6,000-mAh capacity is good for a 70-to-80 percent refill for most iPhones, and the design is rounded with a soft finish, though it is a little thick. It maxes out at 15 watts for charging, with a USB-C port that can hit 20 watts.
Vonmählen Evergreen Mag Magnetic Power Bank for £60: The real attraction of this magnetic wireless power bank is Vonmählen’s eco credentials. The German manufacturer uses recycled cobalt (27 percent), aluminum (90 percent), and plastics (100 percent) in its power banks. There are no compromises on design or functionality. This MagSafe battery pack is sleek and slim (8.6 mm), boasts Qi2 certification, and offers 15-watt wireless and 20-watt wired charging via USB-C. It’s only available in the UK and Europe now, but it will hopefully land in the US soon.
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Photograph: Simon Hill
Scosche PBQ5MS2 Portable MagSafe Phone Charger for $40: Slim, decent magnets, four LEDs to show remaining power, and a wee USB-C cable in the box—so far, so familiar. There’s nothing really wrong with this 5,000-mAh MagSafe power bank, but charging (wireless and wired) maxes out at 10 watts, and you can get better performers for the same money above.
Burga Magnetic Power Bank for $100: If you are appalled at the idea of attaching an ugly limpet to your iPhone, consider splashing out for one of Burga’s stylish MagSafe power banks. A mix of tempered glass and anodized steel, these pretty power banks come in a wide range of eye-catching designs. The camo model I tested had strong magnets and charged my iPhone 14 Pro wirelessly (7.5 watts) to around 70 percent from dead. The USB-C port can also supply 20 watts. The catch is the relatively high price for the relatively low 5,000-mAh capacity.
Groov-e Power Bank for £29: This affordable MagSafe charger is only available in the UK, but it offers a decent 10,000-mAh capacity with a display that shows the precise percentage remaining. You can get 15-watt wireless charging (7.5 watts for iPhones), and the USB-C port can charge devices at up to 20 watts. It’s a little bulky, but the magnets are strong, and it worked well when tested, offering a full charge for my iPhone 14 Pro with around 30 percent left.
Belkin BoostCharge Wireless Power Bank for $33: With a 5,000-mAh capacity and a handy kickstand, this MagSafe power bank is decent. I like the choice of colors (especially purple), but the magnets feel a bit weak, and the kickstand works best in landscape (it feels unstable in portrait). It fell well short of a full charge for my iPhone 14 Pro.
Bezalel Prelude XR Wireless Power Bank for $120: The clever X-range from Bezalel includes two MagSafe power banks and a wireless charging plug. The XR, which I tested, has a 10,000-mAh capacity, while the smaller X ($80) makes do with 5,000 mAh. The XR is bulky, and the kickstand feels flimsy, but it offers more than enough power to fully charge an iPhone 14 Pro. Both power banks charge iPhones at 7.5 watts, and other Qi wireless phones at up to 15 watts, plus you can pop your AirPods on the other side to charge at 3 watts. They also have USB-C ports that can deliver 20 watts.
Mophie Snap+ Juice Pack Mini for $45: This 5,000-mAh-capacity power bank works well, but it’s a little bigger than it should be. It works with MagSafe iPhones but comes with an optional attachment for non-MagSafe phones. Mophie’s Snap+ Powerstation Stand ($70) offers double the capacity and a kickstand, but it’s chunky.
Avoid These MagSafe Power Banks
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Photograph: Simon Hill
Some of the MagSafe portable chargers we tested aren’t worth your time.
Alogic Matrix Universal Magnetic Power Bank: This lightweight, 5,000-mAh-capacity magnetic power bank has an awkward angular look, but that’s because it’s designed to slide into a 2-in-1 dock, a 3-in-1 dock, and a couple of car docks, much like Anker’s 633 above. Unfortunately, one of the Alogic batteries I tested failed and refused to charge. The one that worked managed to add 74 percent to my iPhone 14 Pro’s battery.
HyperJuice Magnetic Wireless Battery Pack: Yet another 5,000-mAh MagSafe power bank, the HyperJuice looks quite nice with four LEDs and a round power button on the back, but the USB-C port is limited to 12 watts, and it only managed to take my iPhone 14 Pro up to 71 percent.
UAG Lucent Power Kickstand: This MagSafe power bank has a curved design with a soft-touch coating and a tough metal kickstand. Unfortunately, the capacity is only 4,000 mAh, yet it’s as big as some higher-capacity options—or even bigger. It added just shy of 60 percent to my iPhone 14 Pro, charging wirelessly at 7.5 watts. The USB-C goes up to 18 watts, but you can get better power and performance for the money.
Moft Snap Stand Power Set: I like the soft faux-leather finish, and this power bank is comfy in the hand and looks great, but the 3,400-mAh capacity only added 41 percent to my iPhone 14 Pro. It comes with a magnetically attached folding stand and wallet, with perhaps enough room for a couple of cards or emergency cash. I like that it attaches separately so you can ditch the power bank when it’s dead, but keep the stand; it just doesn’t offer enough power.
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Tech
Large language models provide unreliable answers about public services, Open Data Institute finds | Computer Weekly
Popular large language models (LLMs) are unable to provide reliable information about key public services such as health, taxes and benefits, the Open Data Institute (ODI) has found.
Drawing on more than 22,000 LLM prompts designed to reflect the kind of questions people would ask artificial intelligence (AI)-powered chatbots, such as, “How do I apply for universal credit?”, the data raises concerns about whether chatbots can be trusted to give accurate information about government services.
The publication of the research follows the UK government’s announcement of partnerships with Meta and Anthropic at the end of January 2026 to develop AI-powered assistants for navigating public services.
“If language models are to be used safely in citizen-facing services, we need to understand where the technology can be trusted and where it cannot,” said Elena Simperl, the ODI’s director of research.
Responses from models – including Anthropic’s Claude-4.5-Haiku, Google’s Gemini-3-Flash and OpenAI’s ChatGPT-4o – were compared directly with official government sources.
The results showed many correct answers, but also a significant variation in quality, particularly for specialised or less-common queries.
They also showed that chatbots rarely admitted when they didn’t know the answer to a question, and attempted to answer every query even when its responses were incomplete or wrong.
Burying key facts
Chatbots also often provided lengthy responses that buried key facts or extended beyond the information available on government websites, increasing the risk of inaccuracy.
Meta’s Llama 3.1 8B stated that a court order is essential to add an ex-partner’s name to a child’s birth certificate. If followed, this advice would lead to unnecessary stress and financial cost.
ChatGPT-OSS-20B incorrectly advised that a person caring for a child whose parents have died is only eligible for Guardian’s Allowance if they are the guardian of a child who has died.
It also incorrectly stated that the applicant was ineligible if they received other benefits for the child.
Simperl said that for citizens, the research highlights the importance of AI literacy, while for those designing public services, “it suggests caution in rushing towards large or expensive models, which emphasise the need for vendor lock-in, given how quickly the technology is developing. We also need more independent benchmarks, more public testing, and more research into how to make these systems produce precise and reliable answers.”
The second International AI safety report, published on 3 February, made similar findings regarding the reliability of AI-powered systems. Noting that while there have been improvements in recalling factual information since the 2025 safety report, “even leading models continue to give confident but incorrect answers at significant rates”.
Following incorrect advice
It also found highlighted users’ propensity to follow incorrect advice from automated systems generally, including chatbots, “because they overlook cues signalling errors or because they perceive the automation system as superior to their own judgement”.
The ODI’s research also challenges the idea that larger, more resource-intensive models are always a better fit for the public sector, with smaller models delivering comparable results at a lower cost than large, closed-source models such as ChatGPT in many cases.
Simperl warns governments should avoid locking themselves into long-term contracts when models temporarily outperform one another on price or benchmarks.
Commenting on the ODI’s research during a launch event, Andrew Dudfield, head of AI at Full Fact, highlighted that because the government’s position is pro-innovation, regulation is currently framed around principles rather than detailed rules.
“The UK may be adopting AI faster than it is learning how to use it, particularly when it comes to accountability,” he said.
Trustworthiness
Dudfield noted that what makes this work compelling is that it focuses on real user needs, but that trustworthiness needs to be evaluated from the perspective of the person relying on the information, not from the perspective of demonstrating technical capability.
“The real risk is not only hallucination, but the extent to which people trust plausible-sounding responses,” she said.
Asked at the same event if the government should be building its own systems or relying on commercial tools, Richard Pope, researcher at the Bennett School of Public Policy, said the government needs “to be cautious about dependency and sovereignty”.
“AI projects should start small, grow gradually and share what they are learning,” he said, adding that public sector projects should prioritise learning and openness rather than rapid expansion.
Simperl highlighted that AI creates the potential to tailor information for different languages or levels of understanding, but that those opportunities “need to be shaped rather than left to develop without guidance”.
With new AI models launching every week, a January 2026 Gartner study found that the increasingly large volume of unverified and low-quality data generated by AI systems was a clear and present threat to the reliability of LLMs.
Large language models are trained on scraped data from the web, books, research papers and code repositories. While many of these sources already contain AI-generated data, at the current rate of expansion, they may all be populated with it.
Highlighting how future LLMs will be trained more and more with outputs from current ones as the volume of AI-generated data grows, Gartner said there is a risk of models collapsing entirely under the accumulated weight of their own hallucinations and inaccurate realities.
Managing vice-president Wan Fui Chan said that organisations could no longer implicitly trust data, or assume it was even generated by a human.
Chan added that as AI-generated data becomes more prevalent, regulatory requirements for verifying “AI-free” data will intensify in many regions.
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