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Modders do what AMD can’t (or won’t) by adding FSR 4 support to more games

  • AMD’s FSR 4 can now be used in more games that don’t have an official implementation, thanks to the Optiscaler mod
  • It isn’t compatible with all games, as FSR 4 reportedly doesn’t support Vulkan yet
  • Players may run into trouble using the mod in anti-cheat games

The reception to AMD’s Radeon RX 9070 series GPU launch was mixed considering the inflated prices, and lack of availability for some. However, if you were fortunate enough to land one at MSRP, a new and improved mod may make life a little easier regarding AMD‘s new upscaling method and its compatibility in games.

As reported by VideoCardz, modders have managed to implement AMD FSR 4 support in numerous titles that already have DLSS or XeSS (Nvidia and Intel’s similar technologies, respectively). This is all thanks to a mod known as Optiscaler on GitHub from cdozdil, which has previously been used to enable other older versions of upscaling methods in titles that don’t have official support.

It’s an important mod for Radeon RX 9070 and RX 9070 XT users to take advantage of since so far, there are only a few titles that have official FSR 4 implementation from game developers. FSR 4 significantly enhances visual quality, particularly with its performance mode – which is arguably a game changer as previous FSR models suffered from ghosting issues that caused a blurry image or trail left behind by in-game UI or character models when in motion.

As well as super-resolution (Xe Super Sampling for Intel’s XeSS), frame generation can also be injected in unsupported games – this is similar to Nukem (on GitHub) which uses DLSS’ Frame Generation in games to implement FSR 3’s frame generation.

While it certainly isn’t as well polished as official support (it’s currently an experimental addition), it could be enough to hold users over for the time being. It’s worth noting that not all games are supported on Optiscaler as of now, which is supposedly because FSR 4 doesn’t support Vulkan (a graphics API used for rendering in plenty of games) yet.

There’s no guarantee that certain titles will even get official FSR 4 implementation – it took CD Projekt Red several months to add FSR 3 to Cyberpunk 2077 (likely because of its partnership with Nvidia), so don’t expect it to happen overnight with FSR 4 – especially with other titles that share a similar agreement with Nvidia.

The AMD Radeon Graphics badge displayed over an RGB gaming keyboard.

(Image credit: Ralf Liebhold / Shutterstock)

Modding capabilities as such should be allowed on anti-cheat games

While mods like this are great for RDNA 4 users and those who can’t use frame generation (mostly owners of Nvidia RTX 3000 series and older GPUs), the only major downside is that it doesn’t seem to work with games that use anti-cheating tools.

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Titles like Elden Ring, Warhammer 40,000: Space Marine 2, and The Finals, use anti-cheat software which is used to prevent cheating online. While I won’t argue against these measures (even though they can ruin performance in some games), they make mods like Optiscaler effectively useless, as users could be banned if they are using it.. Now, I haven’t seen cases of this myself without players genuinely cheating, but it doesn’t mean it’s impossible either.

I’ve shared the same frustrations when it comes to games that don’t support ultrawide resolutions and aspect ratios – those games usually require modification, as evident in Street Fighter 6 which cannot be played at 21:9 or 32:9 aspect ratios unless you use RE Framework by Praydog on GitHub, but Capcom views modding as cheating.

It’s a very similar scenario in this case – gamers spend hard-earned money to acquire new hardware, and if you can’t even use upscaling methods like FSR 4 in a large number of titles, mods like Optiscaler are the only way. So, with those anti-cheat measurements, maybe dial it down a little…Please?

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DeepSeek is rushing to get its next-gen R2 model out sooner than expected

After taking the world by storm with the debut of its R1 reasoning model in January, Chinese AI startup DeepSeek is reportedly looking to maintain the momentum by rushing its new R2 model to market as quickly as possible, Reuters reports.

DeepSeek at first planned to launch R2 in early May, but sources familiar with the company tell Reuters that DeepSeek wants to speed up the schedule. However, the sources didn’t provide a new release date for DeepSeek-R2, which has yet to be announced.

We don’t know much about DeepSeek’s next AI model yet, but the Chinese company wants R2 to have improved coding skills and reason in languages other than English.

When DeepSeek-R1 launched, the entire industry was taken aback by the research paper that claimed the highly sophisticated model was trained at a fraction of the cost of OpenAI’s o1. The pushback was immediate, though, as OpenAI posited that DeepSeek distilled ChatGPT to train its model, and Google called DeepSeek’s claims “exaggerated.”

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Nevertheless, many companies were quick to adopt the new model, including OpenAI investor Microsoft, which added DeepSeek-R1 to Azure AI Foundry and GitHub. You can also find R1 in the Amazon Web Services (AWS) model catalog.

With the arrival of GPT-4.5 still weeks away and GPT-5 potentially months out, DeepSeek has a chance to shake up the market once again if R2 launches soon.

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North Korean hackers created new macOS malware disguised as popular app installers

Another day, another macOS malware is trying to actively exploit your Mac. This time, North Korean hackers are using fake job offers hidden in updates to popular apps like Zoom and Google Chrome to invade your Mac.

As security researchers from SentinelLabs (via AppleInsider) reported, North Korean hackers are pushing the macOS Ferret family of malware. Even though Apple has successfully prevented some of these viruses with the on-device malware tool XProtect, caution is still recommended.

This is not the first time someone has tried to install malware on people’s Macs using the “Contagious Interview campaign” method. Basically, targets are asked to go on an interview through a link that shows an error message and a request to install or update some required software, such as Zoom or Google Chrome. After all, who hasn’t tried to join a call only to have Zoom or WebEx ask for an update?

Thankfully, the macOS 15.3 update added a few new security improvements to prevent this malware from infecting your Mac. However, some of the Ferret viruses can still bypass Apple’s security.

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The researchers from SentinelLabs write: “The ‘Contagious Interview’ campaign and the FERRET family of malware represent an ongoing and active campaign, with threat actors pivoting from signed applications to functionally similar unsigned versions as required. Diverse tactics help the threat actors deliver malware to a variety of targets in the developer community, both in targeted efforts and what appears to be more ‘scatter gun’ approaches via social media and code sharing sites like Github.”

How do you protect yourself from this macOS malware threat?

The best way to protect yourself from this macOS malware threat is to ensure you have the official apps downloaded on your Mac. For example, instead of taking web Zoom calls, make sure to always have them on your Mac app. The same is worth it for WebEx. For Google Chrome, don’t forget to check updates through the browser itself. In addition, having the latest macOS update can guarantee you’re protected against the latest threats as well.

Keep checking BGR for the latest macOS malware trying to exploit your Mac and more.

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DeepSeek: Welcome to US artificial intelligence’s Sputnik moment

Following last weekend’s introduction of the latest large language model (LLM) from DeepSeek, ChatGPT’s new artificial intelligence (AI) rival has topped the Apple App Store for iPhone downloads.

The DeepSeek R1 LLM is open source and uses reasoning combined with what the company calls “cold start data”, which means that rather than trawling the internet and social media sites to amass vast quantities of machine learning data, it relies instead on reinforced learning to improve accuracy.

On its GitHub page, the developers of DeepSeek describe R1 as a large-scale reinforcement learning on the base model. “We directly apply reinforcement learning to the base model without relying on supervised fine-tuning as a preliminary step,” it says. “This approach allows the model to explore chain-of-thought for solving complex problems.”

An estimated 2.1 million searches for DeepSeek were recorded over the weekend, with at least 1.6 million of these on Sunday 26 January alone. This is 12.3% of ChatGPT’s 13 million searches in the same timeframe.

Along with taking a different approach to ChatGPT, the interest in DeepSeek is also being driven by competitive pricing and the fact that the code is open source.

While OpenAI, the maker of ChatGPT, charges $2.50 per million input tokens for its GPT-4o model, DeepSeek is priced at $0.14 per million input tokens in situations where the AI engine is able to draw on previously cached information. Non-cached inputs are priced at $0.55 per million tokens.

The extent of interest in the AI from the Chinese firm resulted in turmoil in the valuation of tech stocks in the US. Reuters reported that Nvidia saw its share price drop 17%, which effectively wiped $593bn off its market valuation.

Wake-up call

In a speech on Monday, US president Donald Trump described DeepSeek as a wake-up call for the US tech sector.

Among the numerous subjects Trump spoke about in his speech to Republican party members of Congress were the executive orders revoking the AI regulations introduced under former president Joe Biden. “We don’t want to have any future president ever sabotage our economy with out-of-control regulations,” he said. “Last week I signed an order revoking Joe Biden’s destructive artificial intelligence regulations so that AI companies can once again focus on being the best, not just being the most woke.”

He then referenced DeepSeek as he continued talking about why deregulation is important for AI in the US. “Today and over the last couple of days I’ve been reading about China and [one Chinese company] in particular coming up with a faster method of AI and a much less expensive method. Hopefully the release of DeepSeek AI from a Chinese company should be a wake-up call for our industries that we need to be laser-focused on competing to win.”

DeepSeek’s developers have been able to combine cutting-edge algorithms to slash the energy demands of AI training and deployment. In his speech, Trump described what DeepSeek had achieved as “good”, since companies aiming to develop AI applications that use DeepSeek do not have to spend as much money compared with rival LLMs. “I view that as a positive, as an asset,” he added.
 
Commenting on what the rise of DeepSeek has meant to financial markets, Charu Chanana, chief investment strategist at investment platform Saxo, pointed out that DeepSeek took only two months to develop and less than $6m to build, using reduced-capability chips from Nvidia. This is significant given that the Biden administration banned the export of high-end Nvidia graphics processors (GPUs) to China in 2023.

“US tech companies are trading at premium valuations, with major AI players like Nvidia, Microsoft and Alphabet commanding forward P/E [price to earnings] multiples far above historical averages,” she said. “With these stocks priced for perfection, even minor disruptions, such as DeepSeek proving advanced AI can be built without top-tier chips, could weigh heavily on share prices. For Nvidia, in particular, its role as a key supplier of AI chips makes it vulnerable if demand for its high-end products wanes.”

The idea of lower-cost and more energy-efficient AI coming from DeepSeek appears to have an immediate impact both on the US tech giants and the energy sector, which has been banking on the growth of AI-fuelled power consumption.

“DeepSeek’s breakthrough signals a shift toward efficiency in AI, which will redefine both energy and AI markets,” said Nigel Green, the CEO of global financial advisory giant DeVere Group. “The opportunities for investors willing to act now are enormous.

“This challenges the assumption that AI’s growth is tied to ever-increasing energy consumption. While the market is reacting to short-term uncertainty, efficiency-driven AI models will expand adoption into new markets and industries. This means more widespread use, deeper integration and, ultimately, sustained demand for energy solutions.”

Arguably, it’s the fact that DeepSeek has been able to achieve results using inferior hardware and offer its LLM at a highly competitive price that is set to change every organisation’s approach to AI: it doesn’t necessarily require throwing vast amounts of costly GPUs at the hardware and having to recoup these costs by charging end users a premium.

“By developing cutting-edge generative AI models without relying on the latest, most expensive hardware, DeepSeek has demonstrated that agility and strategy can outpace raw computational power,” said Kjell Carlsson, head of AI strategy at Domino Data Lab. “Their achievements also highlight the vulnerability of incumbents in the generative AI space – proving that open-source innovation continues to be a powerful equaliser, enabling challengers to match and even surpass established players years into the revolution.”

What all this means is that DeepSeek signifies Chinese competition to Silicon Valley’s existing AI models and is a demonstration of how the pace of AI development is pushing boundaries and lowering costs. 

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Microsoft just added DeepSeek R1 to Azure AI Foundry and GitHub

When it comes to artificial intelligence, Microsoft refuses to be left behind. On Wednesday, the Redmond company announced that the R1 model from DeepSeek is now available on Azure AI Foundry and GitHub. This surprisingly sudden move comes despite the fact that OpenAI claims DeepSeek built AI models using its data without permission.

“As part of Azure AI Foundry, DeepSeek R1 is accessible on a trusted, scalable, and enterprise-ready platform, enabling businesses to seamlessly integrate advanced AI while meeting SLAs, security, and responsible AI commitments—all backed by Microsoft’s reliability and innovation,” Microsoft CVP Asha Sharma said in a blog post.

Sharma also repeated DeepSeek’s pitch for R1, explaining that its power and low cost will give more users access to state-of-the-art AI without heavy investment.

Of course, Microsoft understands the concerns raised about DeepSeek during its rapid rise to prominence in recent weeks, including the sheer amount of data the Chinese company collects. According to Microsoft, the model “has undergone rigorous red teaming and safety evaluations, including automated assessments of model behavior and extensive security reviews to mitigate potential risks.” Plus, Azure AI has tools like content filtering and the ability to test applications before deployment to protect developers and end users.

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If you want to test out DeepSeek R1 through Azure AI Foundry, you will need an Azure account. Once you’re signed in, search for “DeepSeek R1” in the model catalog. After opening the model card, click “Deploy” to obtain the inference API, the key, and access to the playground. You can try out your prompts in the playground to try out R1.

You can also “explore additional resources and step-by-step guides to integrate DeepSeek R1 seamlessly into your applications” on GitHub. Microsoft says Copilot+ PC owners will soon be able to run distilled versions of DeepSeek R1 locally as well.

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Where IT comes from: Behind the scenes at Pure Storage’s European R&D centre

You’re a $2.8bn storage supplier with flash arrays at the core of your business. How do you do research and development (R&D), test new products, test customer workload issues, and test array products over years-long timescales for issues that only arise as software, network and application changes concatenate and interact?

Meanwhile, you are a global business with R&D and developer teams across time zones, all at work on ongoing monthly and quarterly updates, and incessant efforts to optimise storage controller software.

The base product is essentially the same, so effective collaboration and information sharing between teams spread across continents is key. But at the same time, you must also test regional customer-specific array configurations.

The Pure Storage solution is to divide responsibility between hardware and software, while also sharing specific R&D and testing capability between three sites.

There is Santa Clara in California, which is its global headquarters and handles hardware and software R&D and testing. There is also Bangalore in India, which only carries out software R&D and testing.

And there is Prague in Czechia, which recently opened its doors to the IT press. Here, we take a look at what goes on behind the scenes in R&D and product testing at Pure Storage (and its nearby array assembly operation).

Capabilities across the three centres are in many ways duplicated, which sounds counter-productive. But it’s not quite as simple as that, according to engineering vice-president and Pure’s Prague site leader Paul Melmon.

“Generally speaking, the same capabilities exist across all sites, except Santa Clara with its hardware development facilities,” he says.

“We try to make projects run autonomously and to minimise cross-time zone meetings,” says Melmon, adding that information can be shared globally in other ways, such as in Git repositories. 

“Lots of companies split projects into many pieces and distribute them,” says chief technology officer Rob Lee. “We choose significant parts of individual products and give them to individual sites, and have product managers for specific products sitting locally.”

“We give a lot of thought to what to centralise and what to not,” says Melmon. “We have rules of engagement that aim at communications that can minimise the number of meetings.”

R&D, testing and talent in Prague

As mentioned, Pure’s Prague site is dedicated to software research, development and testing. It runs thousands of ongoing and custom test routines on hundreds of racks of software. These are divided into “persistent” and “non-persistent” testing, says engineering manager Tom Healy.

Non-persistent is ephemeral. It tests for issues in specific customer deployment configurations, or the impact of updates on controller code.

Persistent testing is long-term. It can be very long-term, in fact, with racks in place with, for example, generation upon generation of Pure Storage FlashBlade file and object storage deployed.

“Sometimes things can take years to occur,” says Healy.

“Our testbeds include, for example, FlashBlade capacity that dates back to the first generation [2016], some virtualisation, and a Windows application platform. All of this will run for years, to follow the lifecycle of customer systems and to test for the effect of changes to software and hardware, and its stability,” says Healy.

“And, FlashBlade uses Ethernet, so we are checking what happens when changes happen in the workload, simulation of new cabling, media, hitting it with broadcast storms, simulating signal degradation, etc,” he adds. 

Meanwhile, at the Prague facility, hundreds of engineers work constantly on storage array software to meet ongoing monthly and quarterly updates. 

Pure’s Prague R&D facility has just celebrated its five-year anniversary. It is resident in the Amazon building (no relation) and others in the riverside Karlin district. There it employs 600 people – 50% Czech, 50% from elsewhere – with up to 50 nationalities on-site.

Prague was chosen as a European centre because of its proximity to so many of Pure’s customers, but also, says Melmon, because of the availability of talent. “It’s on a level with Silicon Valley,” he says, and its accessibility in terms of transport links, universities, graduates in computer science, cost of living and general likeability of the city.

Speaking of the River City complex in which Pure is located, Melmon describes it as having a “South of Market” feel, referring to the fashionable area of San Francisco that became a honeypot for startups in the 1990s. “It’s the place to be if you’re in tech and AI [artificial intelligence]. There are meetups in the evening. It’s the cool new place to be in Prague.”

But it’s not just a cool place to work and live. Melmon points to the 19.7% figure, which is the proportion of revenue Pure spends on R&D. 

Prague is the biggest Pure Storage R&D centre outside the US and has delivered about a third of its FlashArray product development. Meanwhile, FlashBlade//S was jointly designed and tested there, while key elements of the Pure Fusion workload management platform and its Pure1 AIOps were developed in the Czech capital. Meanwhile, 100% of Portworx Data Services and Pure’s disaster recovery as a service (DRaaS) offering came from there too.

That’s the result, with Prague as a key pillar in the three-site R&D and testing strategy of Pure Storage.

Nearby, also in Czechia, is one of its global assembly centres, which you can read about here.

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Open-R1 is a truly open version of DeepSeek AI

On Monday, DeepSeek R1 crashed the stock market once it became clear to some of the investors trading AI-related stocks that the Chinese startup had found a way to train AI as capable as ChatGPT o1 without access to the state-of-the-art NVIDIA chips that OpenAI and US AI firms have access to. That’s why firms creating hardware for AI infrastructure suffered the most. NVIDIA shed nearly $600 billion in market cap, while the entire market lost almost $1 trillion.

I said at the time that the reactions might be blown out of proportion. Yes, DeepSeek employed software optimizations to develop AI as capable as o1 instead of relying on hardware. But that doesn’t mean NVIDIA’s GPUs are suddenly obsolete. It just realigns the playing field while providing a new way to innovate.

I still think that AI firms with access to the latest hardware and top-tier software talent will have an edge over Chinese rivals. All a company like OpenAI or Google has to do is replicate some of the tricks DeepSeek used to match the Chinese startup’s AI training and usage efficiency and then leapfrog it. The latest AI chips will still be very important here.

It turns out it’s not just the big AI firms that might try to copy what DeepSeek has done. A team of developers calling themselves Open-R1 wants to replicate the DeepSeek R1 success to create a reasoning AI model that’s just as powerful as R1. There’s a big twist in all of this that AI fans in Western markets will appreciate. Open-R1 should be even more transparent than DeepSeek R1.

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DeepSeek’s decision to make its AI models open-source was brilliant. This ensured that anyone could access and install the model on their computer. From there, they’d have a local model as capable as ChatGPT o1. The open-source route would also drive up adoption and testing. News about R1’s capabilities would spread rapidly.

But, as the Open-R1 researchers explain on Hugging Face, DeepSeek R1 isn’t fully open-source:

The release of DeepSeek-R1 is an amazing boon for the community, but they didn’t release everything—although the model weights are open, the datasets and code used to train the model are not .

That’s where Open-R1 is coming in: 

The goal of Open-R1 is to build these last missing pieces so that the whole research and industry community can build similar or better models using these recipes and datasets. And by doing this in the open, everybody in the community can contribute!

Specifically, the Open-R1 team wants to answer the following questions about DeepSeek R1 while they develop an identical AI:

Data collection: How were the reasoning-specific datasets curated?

Model training: No training code was released by DeepSeek, so it is unknown which hyperparameters work best and how they differ across different model families and scales.

Scaling laws: What are the compute and data trade-offs in training reasoning models?

The researchers plan to clone DeepSeek’s development strategy for R1, further fine-tune it, and create a truly open-source Open-R1 model that anyone could use.

Interestingly, the Open-R1 researchers want to distill DeepSeek R1 and create a high-quality reasoning dataset. DeepSeek might have done its own distillation, with OpenAI claiming the Chinese startup used ChatGPT to train its earlier versions of AI. That work might have been critical to getting to DeepSeek R1. It’s unclear if OpenAI can prove these allegations with absolute certainty.

However, the Open-R1 researchers have their own strategy after distilling R1, with the blog explaining how they plan to go forward.

If successful, Open-R1 could be a stepping-stone for developing other sophisticated AI models, and anyone could do it. The advantage here is that you would not have to go through the same training process. Conversely, that’s what OpenAI says DeepSeek did with ChatGPT, using some of its outputs to save money on training the AI.

An open-source reasoning model like the Open-R1 model the researchers propose could be used for other purposes, not just math and coding. The researchers mention medicine, where reasoning AI “could have significant impact.”

That said, it’s unclear how long the project will take and when Open-R1 will be ready for testing. Other AI researchers interested in Open-R1 can check out the project on GitHub.

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Will Europe be the first region to enact regulation for green software?

So far, there is no regulation anywhere in the world specific to the environmental impact of software – a fact that runs alongside the reality that neither consumers nor investors are towards or away from companies based on the green credentials of their software.  

Many experts expect Europe to be the first region to enact regulation that enforces green software practices. One of them is Santiago Fontanarrosa, vice-president of technology at Globant, a digital services company and author of the book Green software engineering: exploring green technology for sustainable IT solutions.

According to Fontanarrosa, Europe is well-positioned to lead in green software regulation thanks in part to its strong sustainability initiatives and advancements in software engineering. Europe is commitment to sustainability, as demonstrated by ambitious initiatives like the European Green Deal. Moreover, France leads in green software research, and Germany’s Blue Angels offers the first global eco-friendly software certification.  

Fontanarrosa said green software is not only about applying certain development practices, it’s also about how to deploy and use the resulting applications. As for what developers can do, many of the green software techniques can be taken from the practices used by people who wrote programs in the 1970s, when CPUs were much less powerful, and memory and storage were much more limited. As processors became faster and memory and storage grew, software engineers have become more complacent. 

“Today, my iPhone has more computing power than the machine I used when I started working in the 1990s,” says Fontanarrosa. “I have seen a big change since I began my career. Developers have become less concerned about how they use resources, like CPU and memory. And they no longer apply optimisation techniques. For example, when you have an algorithm that does a loop to go through a very long list, they don’t look for ways of making that part of their code more efficient.”  

When it comes to green software, efficiency pertains to how much energy a program consumes to perform its functions. This involves optimising not only the use of CPU time, memory access and I/O, but also the transfer of data over networks. If coders simply thought more about the physical operations going on underneath their code, they would develop greener software. 

For example, as compared to a program that periodically checks for updates, an event-based architecture that reacts only when new data becomes available is more efficient because it reduces the number of network requests. Bigger design decisions are also important – an architect can take into account the fact that energy is cleaner at certain times of the day, and decide to have certain intensive tasks performed during those optimal periods.  

As for deploying software, one of the underlying principles is to minimise the amount of data traveling around networks, while another is to be selective of datacentres. 

“The cloud nowadays is a commodity everyone uses,” says Fontanarrosa. “But the cloud is actually a big datacentre somewhere that consumes a lot of energy. If I can choose a data provider cloud that uses more green energy, that will have a big impact on my carbon footprint.” 

Fontanarrosa also advises developers and operators to reduce the number of instances they’re using on the cloud. “Nowadays, you have a credit card, you do two clicks, and you have a whole new infrastructure up there,” he says. “You don’t even worry about it. That’s the kind of mentality that we need to start changing.” 

One example that illustrates how much of an impact software can have is given by Dutch software guru Danny van Kooten in a 2020 blog post that influenced many other developers to make similar changes.

Van Kooten estimates that he reduced emissions by 59,000 kg of CO₂ per month by making a very small change to his WordPress plugins that run on more than two million websites. That savings is the amount of CO₂ used to fly from Amsterdam to New York five times. He says that assuming the average website receives about 10,000 visitors a month and uses cache to serve returning users, a monthly savings of 10,000 kWh can be achieved for every 1 kilobyte a programmer shaves off of their JavaScript.

Another example is described in Fontanarrosa’s book, where he compares two implementations of the Fibonacci sequence, using the CodeCarbon tool to measure energy consumption. The first implementation used a recursive implementation and the second used an iterative approach with a for-loop. The iterative implementation used 99.34% less energy and reduced CO₂ emissions by 99.35%. 

“This striking difference demonstrates how thoughtful implementation choices in algorithm design can drastically reduce energy consumption and emissions, showcasing the potential for greener and more efficient software development,” says Fontanarrosa. 

Fontanarrosa says that even if governments are not pushing for green software, businesses and consumers can make it a reality. One encouraging sign is that a lot of companies have joined the Green Software Foundation since its inception in May 2021, including Fontanarrosa’s organisation, Globant. 

The mission of the Green Software Foundation – which was founded by Accenture, GitHub, Microsoft and ThoughtWorks – is to “build a trusted ecosystem of people, standards, tooling and best practices for green software”.

According to Green Software Foundation, the ICT sector will account for 14% of the world’s carbon footprint by 2040, most of which will be from smartphones and datacentres. The website says that software developers contribute to global emissions in many ways. One is by producing new versions of their products, which often requires better hardware to run, rendering the existing computers obsolete.  

One encouraging sign of progress is that the Green Software Foundation’s Software Carbon Intensity (SCI) specification recently achieved ISO standard status. However, this is nothing like government-backed regulation as SCI is still a voluntary, industry-driven standard. 

“I encourage everyone to learn about green software,” says Fontanarrosa. “Go to the Green Software Foundation webpage, or any other related resource, to start thinking about it and trying to introduce minor changes in your digital products. Minor changes sum up to a big impact.” 

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Denmark’s AI-powered welfare system fuels mass surveillance

Artificial intelligence (AI) tools used by the Danish welfare authority violate individual privacy, risk discrimination and breach the European Union’s (EU) AI Act’s regulations on social scoring systems, according to analysis from Amnesty International.

Udbetaling Danmark (UDK, or Payout Denmark) – established in 2012 to centralise the payment of various welfare benefits across five municipalities – uses AI-powered algorithms to flag individuals who are considered at the highest risk of committing social benefits fraud for further investigation. These were developed in partnership with ATP, Denmark’s largest pensions processing company, and various private multinational corporations.

The report details how UDK’s fraud control algorithms breach the human rights of social security benefits recipients, including their rights to privacy, equality and social security. It also concludes that the system creates a barrier to accessing social benefits for certain marginalised groups, including people with disabilities, low-income individuals and migrants.

“This mass surveillance has created a social benefits system that risks targeting, rather than supporting, the very people it was meant to protect,” said Hellen Mukiri-Smith, Amnesty International’s researcher on artificial intelligence and human rights.

“The way the Danish automated welfare system operates is eroding individual privacy and undermining human dignity. By deploying fraud control algorithms and traditional surveillance methods to identify social benefits fraud, the authorities are enabling and expanding digitised mass surveillance.”

Amnesty argues that UDK’s fraud detection system likely falls under the “social scoring” ban under the EU’s AI Act, which came into force on 1 August 2024.

The act defines AI social scoring systems as those that “evaluate or classify” individuals or groups based on social behaviour or personal traits, causing “detrimental or unfavourable treatment” of those people.

Mukiri-Smith said: “The information that Amnesty International has collected and analysed suggests that the system used by the UDK and ATP functions as a social scoring system under the new EU Artificial Intelligence law – and should therefore be banned.”

UDK and ATP provided Amnesty with redacted documentation on the design of certain algorithmic systems, and allegedly rejected Amnesty’s requests for a collaborative audit, refusing to provide full access to the code and data used in their algorithms.

The Danish authority also rejected Amnesty’s assessment that its fraud detection system likely falls under the AI Act’s social scoring ban, but did not offer an explanation for this reasoning.

In response to this, Amnesty has called on the European Commission to issue clear guidelines on which AI practices constitute a social scoring system in its AI Act guidance. The organisation has also requested that the Danish authorities stop using the system until it can be confirmed that it does not fall under this ban.

Mukiri-Smith added: “The Danish authorities must urgently implement a clear and legally binding ban on the use of data related to ‘foreign affiliation’ or proxy data in risk scoring for fraud control purposes. They must also ensure robust transparency and adequate oversight in the development and deployment of fraud control algorithms.”

Computer Weekly contacted UDK about the claims made by Amnesty International but received no response by the time of publication.

Violation of privacy

Alongside ATP, UDK uses a system of up to 60 algorithms to identify fraudulent social benefit applications and flag individuals for further investigation by Danish authorities.

To power these models, Danish authorities have enacted laws enabling the extensive collection and merging of personal data from public databases of millions of Danish residents. This includes information on residency status, citizenship, and other data that can also serve as proxies for a person’s race, ethnicity or sexual orientation.

Mukiri-Smith added: “This expansive surveillance machine is used to document and build a panoramic view of a person’s life that is often disconnected from reality. It tracks and monitors where a social benefit claimant lives, works, their travel history, health records, and even their ties to foreign countries.”

Individuals interviewed by Amnesty described the psychological impact of being subjected to surveillance by fraud investigators and case workers. Describing the feeling of being investigated for benefits fraud, Stig Langvad of Dansk Handicap Foundation told Amnesty that it is like “sitting at the end of a gun”.

UDK stated that its collection and merging of personal data to detect social benefits fraud is “legally grounded”.

Exacerbation of structural marginalisation

The report also reveals that the benefits fraud control system developed by UDK and ATP is built on inherently discriminatory structures in Denmark’s legal and social systems, which categorises people and communities based on difference.

According to the report, Danish law already creates a “hostile environment for migrants and people who have been granted refugee status”, with residency requirements for those seeking to claim benefits that disproportionately affect people from non-Western countries, with many refugees in Denmark, including Syria, Afghanistan and Lebanon.

The Really Single fraud control algorithm predicts a person’s family or relationship status to assess risk of benefit fraud in pensions and childcare schemes. One of the parameters employed by the algorithm includes “unusual” or “atypical” living patterns or family arrangements, but contains no clarity on what constitutes such situations, leaving room for dangerously arbitrary decision-making.

Mukiri-Smith added: “People in non-traditional living arrangements – such as those with disabilities who are married but live apart due to their disabilities; older people in relationships who live apart; or those living in a multi-generational household, a common arrangement in migrant communities – are all at risk of being targeted by the Really Single algorithm for further investigation into social benefits fraud.”

Gitte Nielsen, the chairperson of the social and labour market policy committee at Dansk Handicap Foundation, described the feeling of being constantly scrutinised and reassessed: “It is eating you up. A lot of our members … have depression because of this interrogation.”

UDK and ATP additionally use inputs related to “foreign affiliation” in their algorithmic models. For example, the Model Abroad algorithm identifies groups of beneficiaries deemed to have “medium and high-strength ties” to non-EEA countries and prioritises these groups for further investigation.

Amnesty’s research found that algorithms such as these discriminate against people based on factors such as national origin and migration status.

In a response to Amnesty, UDK stated that the use of “citizenship” as a parameter in their algorithms does not constitute processing of sensitive personal information.

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