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UK Police Chief Hails Facial Recognition, Outlines Drone and AI Policing Plans

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Any face in the crowd can be caught in the dragnet of a digital police state.

The steady spread of facial recognition technology onto Britain’s streets is drawing alarm from those who see it as a step toward mass surveillance, even as police leaders celebrate it as a powerful new weapon against crime.
Live Facial Recognition (LFR) is a system that scans people’s faces in public spaces and compares them against watchlists.
Civil liberties groups warn it normalizes biometric monitoring of ordinary citizens, while the Metropolitan Police insist it is already producing results.
Britain’s senior police leadership is promoting these biometric and artificial intelligence systems as central to the future of policing, with commissioner Sir Mark Rowley arguing that such tools are already transforming the way the Met operates.
Speaking to the TechUK trade association, Rowley described Live Facial Recognition (LFR) as a “game-changing tool” and pointed to more than 700 arrests linked to its use so far this year.
Camera vans stationed on streets have been deployed to flag people wanted for serious crimes or those breaking license conditions.
Rowley highlighted a recent deployment at the Notting Hill Carnival, where he joined officers using LFR.
“Every officer I spoke to was energized by the potential,” he said to The Sun. According to the commissioner, the weekend brought 61 arrests, including individuals sought in cases of serious violence and offenses against women and girls.
Rowley claimed that the technology played “a critical role” in making the carnival safer.
Beyond facial recognition, Rowley spoke of expanding the Met’s reliance on drones. “From searching for missing people, to arriving quickly at serious traffic incidents, or replacing the expensive and noisy helicopter at large public events,” he said, “done well, drones will be another tool to help officers make faster, more informed decisions on the ground.”
The commissioner also promoted the V100 program, which draws on data analysis to focus resources on those considered the highest risk to women.
He said this initiative has already led to the conviction of more than 160 offenders he described as “the most prolific and predatory” in London.
Artificial Intelligence is being tested in other areas too, particularly to review CCTV footage.
Rowley noted the labour involved in manually tracing suspects through crowded areas. “Take Oxford Street, with 27 junctions—a trawl to identify a suspect’s route can take two days,” he explained.
“Now imagine telling AI to find clips of a male wearing a red baseball cap between X and Y hours, and getting results in hours. That’s game-changing.”
While the Met portrays these systems as advances in crime prevention, their deployment raises questions about surveillance creeping deeper into everyday life.
Expansions in facial recognition, drone monitoring, and algorithmic analysis are often introduced as matters of efficiency and safety, but they risk building an infrastructure of constant observation where privacy rights are gradually eroded.
Shaun Thompson’s case has already been cited by campaigners as evidence of the risks that come with rolling out facial recognition on public streets.
He was mistakenly identified by the technology, stopped, and treated as though he were a wanted suspect before the error was realized.
Incidents like this highlight the danger of false matches and the lack of safeguards around biometric surveillance.
For ordinary people, the impact is clear: even if you have done nothing wrong, you can still find yourself pulled into a system that treats you as guilty first and asks questions later.

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Artificial Intelligence

What are data centers and why do they matter?

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Data centers may not be visible to most Americans, but they are shaping everything from electricity use to how communities grow.

These facilities house the servers that process nearly all digital activity, from online shopping and streaming to banking and health care. As the backbone of artificial intelligence and cloud computing, they have expanded at a pace few other industries can match.

Research from Synergy Research Group shows the number of hyperscale data centers worldwide doubled in just five years, reaching 1,136 by the end of 2024. The U.S. now accounts for 54% of that total capacity, more than China and Europe combined. Northern Virginia and the Beijing metro area together make up about 20% of the global market.

John Dinsdale, chief analyst with Synergy Research, said in an email to The Center Square that a simple way to describe data centers is to think of them as part of a food chain.

“At the bottom of the food chain, you’re sitting at your desk with a desktop PC or laptop. All the computing power is on your device,” Dinsdale said.

The next step up is a small office server room, which provides shared storage and applications for employees.

“Next up the chain, you can go two different directions (or use a mix),” he explained.

One option is a colocation data center, where companies lease space instead of running their own physical facilities. That model can support a multitude of customers from a single operator, such as Equinix.

The other option is to move to public cloud computing.

“You buy access to computing resources only when you need them, and you only pay for what you use,” Dinsdale said.

Providers like Amazon, Microsoft and Google run massive data centers that support tens of thousands of servers. From the customer perspective, it may feel like having a private system, but in reality, these servers are shared resources supporting many organizations.

Cloud providers now operate at a scale that was “unthinkable ten years ago” and are referred to in the industry as hyperscale, Dinsdale added. These global networks of data centers support millions of customers and users.

“The advent of AI is pushing those data centers to the next level — way more sophisticated technology, and data centers that need to become a lot more powerful,” he said.

What is a data center?

At its simplest, a data center is a secure building filled with rows of servers that store, process and move information across the internet. Almost every digital action passes through them.

“A data center is like a library of server computers that both stores and processes a lot of internet and cloud data we use every day,” said Dr. Ali Mehrizi-Sani, director of the Power and Energy Center at Virginia Tech told The Center Square. “Imagine having thousands of high-performance computers working nonstop doing heavy calculations with their fans on. That will need a lot of power.”

Some are small enough to serve a hospital or university. Others, known as hyperscale facilities, belong to companies such as Amazon, Microsoft, Google and Meta, with footprints large enough to be measured in megawatts of electricity use.

How big is the industry?

Synergy’s analysis shows how dominant the U.S. has become. Fourteen of the world’s top 20 hyperscale data center markets are in the U.S., including Northern Virginia, Dallas and Silicon Valley. Other global hotspots include Greater Beijing, Dublin and Singapore.

In 2024 alone, 137 new hyperscale centers came online, continuing a steady pace of growth. Average facility size is also climbing. Synergy forecasts that total capacity could double again in less than four years, with 130 to 140 new hyperscale centers added annually.

The world’s largest operators are American technology giants. Amazon, Microsoft and Google together account for 59% of hyperscale capacity, followed by Meta, Apple, and companies such as Alibaba, Tencent and ByteDance.

How much power do they use?

Large data centers run by the top firms typically require 30 to 100 megawatts of power. To put that into perspective, one megawatt can power about 750 homes. That means a 50-70 megawatt facility consumes as much electricity as a small city.

“Building one data center is like adding an entirely new town to the grid,” Mehrizi-Sani said. “In fact, in Virginia, data centers already consume about 25% of the electricity in the state. In the United States, that number is about 3 to 4%.”

That demand requires extensive coordination with utilities.

“Data centers connect to the power grid much like other large loads, like factories and even towns do,” Mehrizi-Sani said. “Because they need so much electric power, utilities have to upgrade substations, lines and transformers to support them. Utilities also have to upgrade their control and protection equipment to accommodate the consumption of data centers.”

If not planned carefully, he added, new facilities can strain local power delivery and generation capacity. That is why every major project must undergo engineering reviews before connecting to the grid.

Why now?

The rapid rise of AI has supercharged an already fast-growing sector. Training models and running cloud services requires enormous computing power, which means facilities are being built faster and larger.

“AI and cloud drive the need to data centers,” Mehrizi-Sani said. “Training AI models and running cloud services require massive computing power, which means new data centers have to be built faster and larger than before.”

Dinsdale noted in a report that the industry’s scale has shifted sharply.

“The big difference now is the increased scale of growth. Historically the average size of new data centers was increasing gradually, but this trend has become supercharged in the last few quarters as companies build out AI-oriented infrastructure,” he said.

Why certain states lead the market

Different states and regions offer different advantages. According to a July 2025 report by Synergy Energy Group, Virginia became the leading hub because of relatively low electricity costs when the industry was expanding, availability of land in the early years and proximity to federal agencies and contractors.

Texas and California are also major markets, for reasons ranging from abundant energy to the presence of technology companies.

Internationally, Synergy’s analysis shows that China and Europe each account for about a third of the remaining capacity. Analysts expect growth to spread to other U.S. regions, including the South and Midwest, while markets in India, Australia, Spain and Saudi Arabia increase their share globally.

What is at stake?

For most Americans, data centers are invisible but indispensable. Almost everything digital depends on them.

“Streaming movies, online banking, virtual meetings and classes, weather forecasts, navigation apps, social media like Instagram, online storage and even some healthcare services” all run through data centers, Mehrizi-Sani said.

Synergy’s forecast suggests the trend is unlikely to slow.

“It is also very clear that the United States will continue to dwarf all other countries and regions as the main home for hyperscale infrastructure,” Dinsdale said.

This story is the first in a Center Square series examining how data centers are reshaping electricity demand, costs, tax incentives, the environment and national security.

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Artificial Intelligence

Schools should keep AI in its proper place

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From the Fraser Institute

By Michael Zwaagstra

At the dawn of a new schoolyear, the issue of artificial intelligence (AI) looms large. But innovations have always been a part of classroom instruction.

For example, calculators changed the face of math class forever. Kind of.

Before the invention of calculators, all math equations were done manually. Calculators changed things by making it possible to solve complex equations in seconds and often without thinking much about the problem. All you had to do was punch in the correct numbers and—presto—the answer magically popped up on the screen.

Naturally, this led to some debate among teachers. Some thought there was no longer a need for students to memorize math facts including multiplication tables, while others argued that learning basic skills was still important, regardless of whether calculators were available or not.

With the benefit of several decades of hindsight, the evidence is clear that students still should learn basic math facts. While calculators make it possible to solve equations quickly, students who don’t know, by memory, the order of operations, or basic math facts such as multiplication tables, struggle to solve complex equations.

That’s because people have only a limited amount of working memory available at any given time. By committing basic math facts to their long-term memories, students can free up space in their working memories to tackle challenging math questions. In short, it would be a huge mistake to allow students to get away with not mastering important math skills.

Fast-forward to the present challenge of AI. Just as calculators made it easier to solve math equations, AI programs such as ChatGPT can perform research, correct grammar, and even write essays for students in a matter of seconds.

This leads to an obvious question: What should schools do about students using AI? Some schools have tried to ban AI entirely while others embrace it as a regular tool just like a pencil or a pen. Simply put, AI creates even more ethical questions and instructional challenges for teachers than calculators ever did when they were first introduced in classrooms.

Rather than bury our collective heads in the sand, we should tackle the problem of AI head on.

One of the most important things we can do is identify which activities are immune to AI’s influence. Frankly, this is why in-person tests and exams are more important than ever. If tests are written with pen and paper under a teacher’s supervision, students will not be able to use AI to formulate answers. Thus, rather than abolish tests and exams, with the advent of AI programs, we must embrace formal tests and exams even more than before. And we must use them more regularly.

As for regular assignments, schools should have students complete as much of their work in class as possible. For assignments that must be completed at home, teachers should design questions that are as “AI-proof” as possible. For example, asking students to answer specific questions about something discussed in class is much better than having them write a generic essay on a famous person’s life.

Teachers will need to redesign assignments so that they cannot be easily completed by AI. Students are naturally inclined to follow the path of least resistance. So it’s important for teachers to make it hard for them to get AI to do their homework. That way, most students will conclude it’s better to do the assignment themselves rather than have AI do it for them.

Finally, it makes good sense to allow students to use AI as a tool on some assignments. Since AI is already being used by many professionals to make their jobs easier, it’s a good idea to teach students appropriate ways to use AI. The key is to ensure that students know the difference between using AI as a resource and using it to cheat on an assignment.

AI is here to stay, but that doesn’t mean schools should let this new technology take over the classroom. The key is to keep AI in its proper place.

Michael Zwaagstra

Senior Fellow, Fraser Institute
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