TECH

KAIST Develops AI that reconstructs cloud-covered ground in satellite images with photo-like clarity

Satellite images of the Earth are not always easy to use. Clouds can block the view of the ground, and images captured with certain imaging methods look different from ordinary photographs, which makes them difficult to interpret. A KAIST research team has developed an artificial intelligence (AI) model that learns the appearance of diverse terrain and cities. The model can restore cloud-covered images and convert radar images into images that resemble ordinary photographs. It applies what it learned while generating satellite images to a range of image-processing tasks, so it is expected to expand the use of satellite data in disaster response and environmental monitoring.

KAIST (President Choongsik Bae) announced on October 20 that a research team led by Professor Munchurl Kim of the School of Electrical Engineering has developed GeoCore-9B, an AI model that can be used to generate, restore, and convert satellite images. During training, the model’s core image-generating component learns image features by adjusting about 9 billion internal values, known as parameters. GeoCore-9B is pretrained on a large volume of satellite data and can then be adapted to a variety of tasks. For this reason, it is classified as a ‘foundation model,’ a type of AI model that serves as the basis for a wide range of AI technologies.

Satellite images are essential for monitoring changes in forests and cities and for responding to disasters such as floods and wildfires. However, optical images, which record sunlight reflected from the Earth’s surface, cannot clearly capture the ground when clouds are thick or at night. Synthetic aperture radar (SAR) observes the ground by transmitting radio waves and measuring the signals reflected back. It can acquire images of the Earth’s surface at night and in cloudy weather. However, SAR images differ from ordinary photographs in brightness and patterns, which makes them difficult to interpret.

To create a common basis for processing these different types of satellite data, the research team trained the core image-generating model from scratch on Earth observation data. Instead of adapting the core component of a model built to generate ordinary photographs, the team trained a new model on overhead satellite views of terrain, forests, ports, and cities.

The model was trained on 10 million pairs of satellite images from around the world and sentences describing each image. For example, an image of a port was paired with a description stating that ships and warehouses are located there. An image of a rural area was paired with a description stating that a road runs between fields. By learning from this large collection of described images, the AI learned the features of diverse terrain and urban areas.

A key feature of this study is that the AI learned not only what an image contains, but also which location on Earth the image shows and how large an area it covers. In satellite images, objects with similar shapes may be small buildings or large industrial facilities, depending on their actual size. It is therefore important to take into account both where an image was taken and its spatial scale.

The team trained the model using latitude, longitude, and ground sample distance (GSD) together with the descriptive sentences. Latitude and longitude indicate a position on Earth. GSD indicates the size of the ground area represented by a single pixel, the smallest unit of an image. For example, at a GSD of 1 meter, one pixel represents an area of about 1 meter by 1 meter on the ground. Using this information, the AI learned to generate images that match a given location and spatial scale.

The team also applied a training technique called ‘Geospatial Semantic Alignment,’ which helps the model represent the structure of terrain and cities more accurately. In this technique, the generative model learns by referring to features extracted by an AI specialized in satellite imagery. These features serve as a training reference that helps the model learn structural elements of an image, such as how roads connect and where farmland boundaries lie. The specialized AI is used only during training, so it does not add computation when the model generates images.

The researchers then fine-tuned GeoCore-9B separately for cloud removal and for radar image conversion. They used LoRA (Low-Rank Adaptation), a method that keeps the core model fixed and trains only a small number of additional parameters. This approach reduces the burden of retraining an entire large AI model from scratch for each task.

In evaluations using publicly available satellite datasets, GeoCore-9B outperformed existing task-specific models at restoring cloud-covered images. It scored higher on the key metrics that measure brightness, color, and structural similarity to the reference images. In converting radar images into optical images, it also achieved the best results on some of the metrics that assess the quality and fine detail of the generated images.

These results demonstrate that knowledge gained from generating satellite images can also be applied to restoring and converting actual observation images. The model is expected to make greater use of data whose value has been limited by cloud cover or imaging methods. It is also expected to support the assessment of conditions in disaster areas and the monitoring of changes in forests, farmland, and cities. In addition, adapting a single common model to multiple tasks can reduce the cost and time required to develop a separate AI for each task.

The team plans to validate the model’s performance across a wider range of sensors and regions. To apply the model to real-world disaster response, the team also plans to expand its research to accurately predicting changes on the ground, such as floods, building damage, wildfires, and urban expansion. The team will also explore defense applications such as surveillance and reconnaissance.

“By training the AI on where each satellite image was taken and the actual size of the terrain it shows, we made it possible to use a single model for multiple tasks such as cloud removal and image conversion,” said Professor Kim. He added that he expects the work to make satellite data limited by weather or imaging methods more widely usable, which will help with assessing disaster situations and monitoring changes in forests and cities.

Recent artificial intelligence models can reconstruct cloud-covered ground in satellite imagery with high clarity by combining optical data with radar sensing, historical archives, and specialized generative foundation models.

Key AI technologies for cloud removal and ground reconstruction:

• KAIST GeoCore-9B: Developed by researchers as a 9-billion-parameter generative foundation model trained specifically on Earth observation data, this model performs tasks like cloud removal and SAR-to-optical image translation with consistent layout and sharp boundaries.

• Microsoft SpaceEye: Uses Synthetic Aperture Radar (SAR) from the Sentinel-1 mission (which penetrates clouds) paired with historical optical data to generate daily cloud-free predictions of what the ground looks like underneath.

• ClearSKY Vision: Merges optical and radar (SAR) data to reconstruct missing Sentinel-2 data and deliver analysis-ready, cloud-free imagery.

• SenseNet & CMSN: Deep denoising and frequency-domain learning models (such as coarse-to-refine Residual FFT-Convolution and attention-based networks) isolate and remove cloud/haze structures while reconstructing underlying textures.

• Google AlphaEarth Foundations: Functions as a "virtual satellite" that integrates petabytes of multi-modal Earth observation data to map persistent cloud-covered regions (such as parts of Ecuador or Antarctica).

KAIST

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DIGITAL LIFE

Online Safety Act drives VPN interest and raises privacy concerns

People's online behavior has changed in response to legislation to prevent children from viewing harmful content—raising possible privacy and security risks, research suggests.

A new study examined how individuals have responded to age-verification checks enforced by the Online Safety Act (2023) and evaluated the potential risks from bypassing the rules.

The Online Safety Act is a set of U.K. laws and duties that require social media companies and search engines to protect users, especially children, from illegal content and harmful material.

VPN interest surged around age checks...Researchers from the University of Edinburgh, in partnership with the Universities of Newcastle and Durham, studied data from Google Trends and Reddit, a forum-based social media platform.

They found that U.K. users' online discussions about virtual private networks (VPNs)—tools used to circumvent the new restrictions—increased by 145%, while U.K. Google searches for VPNs rose by 147%, using statistical analysis to compare with a typical day.

Discussions relating to the act in U.K. political communities on Reddit also increased by 1,481%.

The increases were concentrated around July 25, 2025, when age checks for adult websites and some social media platforms came into force, researchers say.

While online discussions do not translate directly into VPN usage, the team used this as a proxy for interest, as they were not able to access data on the number of VPN downloads.

Avoiding checks can introduce privacy risks...Age verification may require users to disclose sensitive information about themselves, such as facial scans, identity documents or payment details.

A VPN creates a secure, encrypted connection between users' devices and the internet. It hides individual IP addresses and jumbles data so internet providers cannot track a location or online activity.

However, people who seek to gain access to restricted sites without submitting personal data by using VPN services may be putting themselves at risk, experts say. This is because some high-risk VPN services track, retain and share user data with third parties.

The increase in VPN interest matters because people using these services may not understand the difference between low- and high-risk providers, experts say.

Researchers say there is a need for greater understanding of the privacy risks of VPNs to help keep everyone safe online.

The paper appears in the journal Proceedings on Privacy Enhancing Technologies and was also released on the arXiv preprint server.

Privacy concerns shape users' choices...Dr. Tuğrulcan Elmas, lecturer in the School of Informatics, University of Edinburgh, said, "The sharp rise in interest in VPNs highlights an important unintended consequence: users seeking to avoid sharing sensitive information for age verification could unknowingly use services that collect and sell their data."

Dr. Marc Juarez, lecturer in cybersecurity and privacy, University of Edinburgh, said, "It surprised us that people trust VPN providers over the companies who have been certified to run the checks, especially as user interest also increased for high-risk VPNs. We urgently need to raise awareness of the privacy risks of VPNs and highlight that not all VPN providers are created equal."

Dhyey Mehta, an undergraduate student from the University of Edinburgh, said, "What stood out from our findings was that users did not frame VPNs as a way to bypass restrictions, but as a way to preserve privacy amid identity checks, facial scans and surveillance. Future policies should account for how people actually respond and address the concerns that drive them toward circumvention."

Interest in virtual private networks (VPNs) has risen following the introduction of the Online Safety Act, but Edinburgh academics have warned of the ‘privacy risks’ in using such platforms.

A new study led by the University of Edinburgh examined how individuals have responded to age verification checks enforced by the Online Safety Act and evaluated the potential risks from bypassing the rules.

The 2023 legislation is a set of UK laws and duties that require social media companies and search engines to protect users, especially children, from illegal content and harmful material.

Researchers found UK users’ online discussions about VPNs – tools used to avoid restrictions – increased by 145 per cent, whilst Google searches for VPNs rose by 147 per cent, using statistical analysis to compare with a typical day.

The academics, worked with colleagues from the universities of Newcastle and Durham, studied data from Google Trends and Reddit, the online forum and message board.

On Reddit, discussions relating to the act in UK political communities increased by 1,481 per cent, the study showed.

The increases were concentrated around 25 July 2025, when age checks for adult websites and some social media platforms came into force, researchers say.

Without data on VPN downloads, the team used online discussions as a ‘rough proxy’ for interest, even though discussion doesn’t directly reflect usage.

Age verification may require users to disclose sensitive information about themselves, such as facial scans, identity documents or payment details.

A VPN encrypts the connection between your device and the internet, hiding your IP address and scrambling your data so internet providers can’t see where you are or what you’re doing online.

But experts warn that people using VPNs to reach restricted sites without handing over personal details could be putting themselves at risk.

Some riskier VPN services track, store and share user data with third parties, and many users may not know how to tell safe providers from risky ones, and the researchers said people need to better understand VPN privacy risks to stay safe online.

Dr Tuğrulcan Elmas, lecturer in the School of Informatics, University of Edinburgh, said: “The sharp rise in interest in VPNs highlights an important unintended consequence: users seeking to avoid sharing sensitive information for age verification could unknowingly use services that collect and sell their data.”

Dr Marc Juarez, lecturer in cybersecurity and privacy, University of Edinburgh, added: “It surprised us that people trust VPN providers over the companies who have been certified to run the checks, especially as user interest also increased for high-risk VPNs. We urgently need to raise awareness of the privacy risks of VPNs and highlight that not all VPN providers are created equal.”

The implementation of age-verification guidelines mandated by the UK’s Online Safety Act has triggered a significant side effect: a massive surge in interest in Virtual Private Networks (VPNs) and the emergence of new privacy and digital security risks.

A study published in October 2026 by researchers from the universities of Edinburgh, Durham, and Newcastle revealed how user behavior shifted following the introduction of mandatory age checks.

The VPN interest boom...To map the public's reaction, the researchers analyzed data from Google Trends and discussions on the platform Reddit. Key findings indicate that:

• Google searches: Searches for VPNs in the UK spiked by 147% compared to a typical day.

• Forum discussions: Discussions about VPNs on Reddit rose by 145%, while threads concerning the legislation within British political communities surged by 1,481%.

• Critical moment: This spike in interest was heavily concentrated around July 25, 2025—the date when strict age-verification requirements for adult websites and certain social media platforms came into effect.

Why did users turn to VPNs? The Online Safety Act requires digital platforms to employ "highly effective" systems to prevent minors from accessing harmful content. In practice, this necessitates that users provide highly sensitive information, such as facial scans (biometrics), identification documents, or credit card details.

Many adults and young people have opted to use VPNs to mask their IP addresses, making it appear as though they are browsing from outside the UK. By doing so, they are able to access websites directly, bypassing the data-verification process. 

New privacy risks...Although users adopt VPNs with the goal of protecting their identities and avoiding the exposure of documents on web portals, experts warn that this strategy has created a new privacy hazard:

1. Use of malicious providers: Many users—especially those using such tools for the first time—cannot distinguish between secure services and high-risk or "free" providers.

2. Data collection and sale: Numerous low-quality VPNs profit precisely by tracking, retaining, and selling users' browsing history and personal data to third parties.

3. Shifting the risk: As highlighted by the study's authors, in attempting to protect minors by restricting access, regulators have inadvertently pushed the general population toward services that compromise their digital security.

Government response...Faced with this situation, some lawmakers even suggested restricting or blocking tools used to bypass access controls. However, following intense pressure from digital rights organizations such as the Open Rights Group, the British government backed away from plans to ban VPNs or impose age restrictions on them, acknowledging them as legitimate and essential cybersecurity tools for remote work, journalists, and the general public.

Provided by University of Edinburgh 

 

TECH

The iPhone 18 Pro’s variable aperture is unnecessary for most people

The new camera on the iPhone 18 Pro introduced a feature that garnered significantly more attention at launch than anything else: a variable aperture system for the main (48 MP) lens, capable of switching between f/1.48 and f/4.0. While the company’s marketing might make this sound revolutionary, the reality is that the mechanism will have an almost imperceptible impact on the daily lives of the vast majority of users.

For those who have mastered professional photography and use manual controls, a variable aperture offers precise adjustments over light intake and the natural optical blur (bokeh) of a scene.

However, for the average user who relies solely on automatic mode, the system will remain locked at an f/1.8 aperture almost all the time—behaving exactly as it did in previous iPhone generations.

Is there any real advantage for those who use automatic mode? Furthermore, this feature is far from new to smartphones—even setting aside the Samsung Galaxy S9, which featured a simpler system capable of switching between only two, much more limited, aperture settings.

The only significant practical benefit occurs in extremely low-light environments. In such situations, the lens opens to its widest setting of f/1.48, allowing for greater physical light capture and faster shutter speeds, which helps reduce motion blur. Yet, this same result could be achieved by maintaining a fixed, wider physical aperture and adjusting exposure through image processing—without the need for a moving mechanical component inside the camera module.

For years, brands like Xiaomi and Huawei have incorporated lenses with variable apertures—offering settings similar to those found on the iPhone 18 Pro—into their own high-end smartphones. The most striking example is the Huawei Pura 80 Ultra, which combines this technology with a 1-inch sensor and continues to lead the global DxOMark camera rankings, pushing Apple’s latest release into second place.

If you aren’t a professional content creator—or don’t plan on using camera apps with advanced manual controls—you’ll be paying a premium for moving parts within the sensor that are unlikely to make any real difference to your final results. Your photos will still be excellent, but not because of any lens variability when shooting in automatic mode.

If you have any other questions about the device, check out our full review of the iPhone 18 Pro. In the meantime, we’d love to know: do you typically adjust your camera settings manually before taking a photo, or do you prefer to rely entirely on your phone’s automatic mode?

Testing the iPhone 18 Pro’s most-hyped feature The iPhone 18 Pro has a variable aperture. According to Apple’s keynote, it’s the brand’s «biggest camera improvement in years». Supposedly, it provides «more control for light intake and depth of field,» enabling «new levels of creativity». Well, well. That does sound great.

But is there any truth to these salesy promises? In a bid to find out, I put the feature under the microscope – and got a mixed first impression. In most situations, the variable aperture makes zero difference to your photos. But that doesn’t mean it’s entirely pointless.

Very little control over depth of field...If you’re a photography buff, you’ll be familiar with the concept of a variable aperture from using traditional cameras. It’s a mechanism that uses multiple blades to increase or decrease the size of the lens opening. On larger cameras, variable apertures are indispensable because they serve two important functions:

Controlling light intake: If you’re taking pictures in blazing sunlight, a fully open lens lets in so much light that the camera’s shutter can’t expose the sensor for a short enough period of time. As a result, photos turn out overexposed. A variable aperture solves that problem.

Controlling depth of field: The larger the lens opening, the smaller the area in focus. Photographers often want this, as it means the subject in focus stands out from the background. It’s a technique used in portrait photography, for instance. In other scenarios, however, every area of the photo needs to be equally sharp. Landscape photography is a prime example of this. Variable apertures allow you to control that depth of field.

On a smartphone, these functions are mostly unnecessary. This is down to the device’s tiny image sensor (1/12 of the size of a full-frame sensor) and its electronic shutter. It already achieves high enough shutter speeds – even in direct sunlight with the lens wide open. As a result, you don’t need a variable aperture to get proper exposure.

The small sensor also means that the focal plane is super wide anyway – especially with a wide-angle lens like the one on the iPhone’s main camera (24 mm 35 mm equivalent). The maximum aperture of ƒ/1.48 produces roughly the same depth of field as ƒ/5.2 on a full-frame camera. Photos come out pretty sharp all over, unless you get extremely close to the subject. The new aperture can only make the focal plane larger, not smaller. That means pronounced blurring (known as «bokeh») still has to be created digitally using Portrait Mode.

Strictly speaking, the «more control over exposure and depth of field» promised in Apple’s promotional materials isn’t a lie. However, on a smartphone, the benefits are far less significant than the company suggests.

That being said, the feature does have two minor uses. Firstly, the variable aperture allows you to deliberately increase exposure time to create motion blur. Though, only within a narrow range of ambient light. The iPhone’s aperture can be adjusted from just ƒ/1.48 to ƒ/4. Outdoors, in daylight, the shutter speed is still much too fast, even at ƒ/4. And in very low light, it doesn’t make any difference, because you’ll be opening the aperture all the way anyway. At best, it’ll make a difference in a handful of scenarios – and only at dusk.

The second benefit of the variable aperture is similarly limited. Videos look more natural when the shutter speed of individual frames is about half the frame rate. For example, 1/60 second at a frame rate of 30 frames per second (FPS). This is because our eyes expect some motion blur in videos. Again, in specific lighting situations, the variable aperture can be the deciding factor in whether to set the shutter speed to 1/250 or 1/60 of a second. In most cases, though, you’ll still need an ND filter if you want to pull this off.

The variable aperture on the iPhone 18 Pro (ranging from f/1.48 to f/4.0) has drawn mixed reactions; it is generally considered of little use to the average user, serving more as a niche feature for content creators and photography enthusiasts than as a revolutionary advancement for daily use.

Why don’t most people need it?

• Small sensor: Due to the tiny size of a smartphone sensor, the depth of field is naturally quite deep. The optical background blur (bokeh) produced at f/1.48 is limited—very similar to the effect that software-based portrait modes have been achieving for years.

• Over-reliance on automatic exposure: For everyday photography—such as quick snapshots of family, pets, or food—the iPhone’s automatic mode intelligently adjusts the image, eliminating the need for the user to manually manipulate physical aperture blades.

• Limitations in daylight: In very bright or sunny environments, even when stopping down to f/4.0, the phone’s shutter speed remains extremely fast; this does not fully replace the need for professional-grade filters (such as ND filters).

Who actually benefits from this feature?

• Videographers and content creators: Those who utilize manual controls—such as the 180-degree shutter rule—to achieve more natural, cinematic motion blur in their videos.

• Photography enthusiasts: Those who prefer using "Pro" mode to lock white balance and manually adjust light intake and sharpness for specific conditions (such as during twilight).

 

by mundophone

 

TECH

How to mod NVIDIA DLSS 5 neural rendering into unsupported games for a huge FPS lift

A developer has vibe-coded a new ReShade add-on file that offloads the DLSS Neural Rendering workloads to a second GPU, almost negating the otherwise massive DLSS NR performance hit. Given the original DLSS 5 shots we saw when Jen-Hsun first showed off the feature around GDC and GTC in the Spring were running on two graphics cards, that's not wholly surprising, but this mod certainly does open the the potential for keeping your old GPU around for neural coprocessing in the future.

Like almost all of the current DLSS 5/Neural Rendering implementations we've seen where the user gets to pick the game, the model, and the weight of the effects, the Neural Coprocessor add-on file—mgpu_bridge—is designed for ReShade, an app used to inject different visual effects into PC games. The files exist on Github, can be downloaded and added into a game that's already been modded with DLSS 5 support, and could dramatically improve the performance.

This is, for me, the biggest issue with both the homebrew mods for DLSS Neural Rendering and the only release implementation we've seen from Nvidia and the developers themselves: the hefty performance penalty from turning it on.

Nvidia itself has touted a 50 - 60% frame rate hit as a blanket figure—regardless of game and how much AI filtering it's doing—as what it is expecting to see from enabling DLSS NR. Andy benchmarked NBA 2K27 ahead of launch and found that sometimes the frame rate impact can be even higher than that.

I mean, it's not as bad as when you try and run DLSS NR on AMD GPUs, but I've noted these kinds of performance hits when you run the new feature through ReShade, too. But Marcelo Guibout has coded a ReShade add-on file that allows a second GPU—here an RTX 5060 Ti 16 GB—to take the final finished frame and perform the neural rendering pass on that GPU and output on its own monitor. That means it doesn't have to hand the frame back, which adds its own performance hit and latency.

If you read that headline and you thought "wait, I thought DLSS 5 hurt your FPS", you're right. The idea here is that NBA 2K27 has been updated with the new DLSS 5 Neural Rendering that NVIDIA announced prior to the RTX Spark platform launch, and folks have been replacing the old library with the new one for a big performance lift with very minimal changes in image quality.

There are reports all over X about this; the most convincing is probably this one from Detro (@detro_official) where he compares Judy with the old DLSS 5, the new DLSS 5, and without DLSS 5 on an RTX 4080 SUPER. With the old version his upscale was taking 8.8ms; with the new version, just 5.4ms. His frame rate jumps from 108 FPS to 150 FPS, although we should note those numbers are with 4x multi-frame generation, so the actual frame rates are 27 FPS and 37.5 FPS. That's still a 39% uplift, which is massive for a DLL swap.

The two shots do look a bit different; that could be down to differences in the new model, but it could also potentially be down to a shift in the time of day. That's the reason given for the difference in looks between these two shots from Forza Horizon 6, by Gaming Optimized (@GamingOptimiz):

The performance is again, relatively low—jumping from 27 FPS to 37 FPS—but it's only a GeForce RTX 4060 card. In that context, it's actually quite impressive that the GPU manages to pull out 37 FPS average with DLSS 5 enabled in Forza Horizon 6! That same user posted a comparison in Resident Evil 9, as well:

There's definitely some image quality difference between the older and newer model, as Resident Evil 9 does not have a dynamic time of day system like the others. However, the difference is decidedly minor, and that's incredibly impressive considering how much faster the new model is. If NVIDIA can continue making gains like this, it's possible that Neural Rendering might become mainstream.

Of course, that depends entirely upon the game you're using it with, and whether its art style suits the photorealistic look of DLSS 5 NR. For example, I personally don't particularly care for the effects of DLSS 5 NR on Judy's face in Cyberpunk 2077 (top). However, for games that are less stylized (like NBA 2K27), it definitely gives a wild uplift in the visuals, and this performance gain is absolutely welcome.

If you want to try this out for yourself, it's not exactly a straightforward process. You're going to have to download ReShade (the Addons version, which makes it unsuitable for online games), and join the RenoDX Discord to grab the modified version that allows you to inject DLSS 5. You'll also need the new version of the DLL, currently hosted on X here, although who knows how long that will stay up. Once you have those, put everything into the same directory with the game executables, and then when in-game, press the "Home" button (by default) to customize the effect. There's a more detailed guide in the RenoDX Discord, so head over to their website if you want to join.

 

by mundophone

 

TECH

NVIDIA GeForce NOW unlocks 4X frame gen with full DLSS 4.5 controls

NVIDIA continues to tune its GeForce NOW cloud game streaming service with a steady rollout of upgrades and capabilities, the latest of which sees the service gain DLSS 4.5 knobs and dials for more granular DLSS control, complete with Ray Reconstruction and up to 4X Dynamic Multi-Frame Generation. The caveat is that the newest bells and whistles are exclusive to the top subscription tier.

Specifically, that means you'll need a GeForce NOW Ultimate membership to take full advantage. Priced at $19.99 per month or $199.99 per year (which equates to $16.67 per month), Ultimate subscribers streaming from NVIDIA's highest-performance cloud rigs get access to pods equipped with GeForce RTX 5080-class hardware. Other perks include support for up to a 5K resolution, gaming at up to 360 frames per second, NVIDIA Reflex support, Cloud G-Sync, and up to 8-hour gaming sessions, on top of everything included in the Performance tier (like HDR10 and SDR10 support, ultrawide monitor support, and persistent in-game settings).

The addition of DLSS 4.5 controls with 4X Frame Gen serves as another selling point to splurge on the top subscription tier.  DLSS 4.5 override controls are available on multiple platforms too, including Windows, macOS, Chrome OS, Linux, and SteamOS. Same goes for browser apps, including Chrome (Windows, macOS, and Chromebook), Edge (Windows only), Firefox (Windows only), Opera GX (Windows or macOS), and Safari (macOS).

In a support document, NVIDIA notes that "compatibility and official DLSS support can vary by game and override type," and so it recommends that most users select the "Recommended" option before playing around with manual overrides.

Outside of the new controls, NVIDIA added 10 more games to its streaming library:

Deep Dish Dungeon (New release on Steam, Oct. 13)

Planet Zoo 2 (New release on Steam, Oct. 13)

Total War: SHOGUN 2 – Complete Edition (New release on Steam, Oct. 13)

Valor Mortis (New release on Steam, Oct. 13)

Among the Trolls (New release on Steam, Oct. 14)

over the hill (New release on Steam, Oct. 14)

Warhammer 40,000: Boltgun 2 (New release on Steam, Oct. 14)

Beyond These Stars (New release on Steam and Xbox, Oct. 15), available on Game Pass

Tenebris Somnia (New release on Steam, Oct. 16)

The Witcher 3: Wild Hunt – Remastered (Xbox), available on Microsoft Store

Users who want to try out the new GeForce NOW controls without committing to a month or year of service can purchase a day pass, which is priced at $7.99 for Ultimate.

mundophone

TECH

RTX Spark vs M5 Pro

A direct comparison has been published between NVIDIA's RTX Spark and Apple's M5 Pro, evaluating which chip delivers the better laptop experience for users who need both productivity performance and GPU-accelerated workloads. The comparison comes at a pivotal moment, as RTX Spark represents NVIDIA's first entry into the PC CPU market with an Arm-based system-on-chip that integrates a desktop-class Blackwell GPU directly alongside ARM CPU cores, while Apple's M5 Pro is the latest iteration of the company's custom silicon that has defined premium laptop performance since the M1 debut in 2020.

On the CPU front, Trusted Reviews notes that Apple's M5 Pro holds advantages in single-threaded performance thanks to years of hardware-software co-design and TSMC's latest manufacturing process. However, RTX Spark's 20-core Arm CPU closes the gap significantly in multi-threaded workloads, particularly in scenarios that benefit from the chip's unified memory architecture and NVLink-C2C interconnect providing 600 GB/s of bandwidth between CPU and GPU. The comparison highlights that while Apple's lead in raw CPU benchmarks persists, the performance gap has narrowed to levels where real-world differences in productivity applications are minimal.

Where the comparison tilts decisively in RTX Spark's favor is GPU-accelerated workloads. The integrated Blackwell GPU with 6,144 CUDA cores — matching the desktop RTX 5070 — supports DLSS 4.5 with Multi-Frame Generation, full DirectX 12 Ultimate ray tracing, and can run AI models with up to 120 billion parameters locally. Trusted Reviews notes that for gaming, 3D rendering, and on-device AI inference, RTX Spark laptops offer capabilities that Apple's M5 Pro cannot match, making them a compelling choice for users who need workstation-class GPU performance in a portable form factor. The article concludes that the choice between the two platforms will largely depend on whether users prioritize Apple's ecosystem integration and proven battery life, or RTX Spark's superior GPU performance and AI capabilities when devices ship later this year.

Nvidia’s RTX Spark “Superchip” grabbed all the attention when it was first announced in June. At the time, it was revealed that RTX Spark-powered laptops will be made by Asus, Dell, HP, Lenovo, MSI, and Microsoft. Earlier this month, Microsoft unveiled the Surface Laptop Ultra with a 20-core Nvidia Grace CPU and up to 128 GB of LPDDR5X unified memory. Now, this machine has been tested against the much cheaper M5 Pro-powered MacBook Pro with some interesting results.

Starting with CPU benchmarks, the Surface Laptop Ultra (20-core CPU, 128 GB RAM) was consistently slower than the MacBook Pro (M5 Pro, 64 GB RAM). The testing included Cinebench 2026, Cinebench 2024, and Blender. In some cases, it was even slower than the Legion Pro 5i (Intel Core Ultra 9 275HX, 130W) and the HP ZBook Ultra (Ryzen AI Max+ Pro 395, 70W). We saw similar results with the Asus ProArt P16, which is even more expensive (as tested) than the Surface Laptop Ultra.

Creative workflows...It may come as no surprise to some, but the M5 Pro-powered MacBook Pro (14-inch) consistently beat the Surface Laptop Ultra with its 20-core N1X chip. Hardware Canucks posted their results showing comparisons in several creativity tools such as Adobe Photoshop, Lightroom, DaVinci Resolve Studio, as well as some AI workflows. Keep in mind that Nvidia has designed and marketed the RTX Spark platform to be an AI workhorse.

In every test, the Surface Laptop Ultra lagged behind the MacBook Pro. With the Adobe suite of applications, both Photoshop and Lightroom had the Surface Laptop Ultra (80W) trailing far behind. Photoshop, in particular, had it at the bottom, performing worse than the Intel Core Ultra 9 275HX (130W) and the Ryzen AI Max+ Pro 395 (70W).

On the GPU side, Premiere Pro and DaVinci Resolve performance was compared, and the Surface Laptop Ultra’s 6,144 CUDA-core GPU was a bit slower than the M5 Pro’s 20-core GPU (15W). Notably, Premiere Pro isn’t yet optimized for the RTX Spark platform, and DaVinci Resolve is heavily optimized for Apple’s M-series chips.

AI workflows...Coming to running LLMs, specifically the GGUF (GPT-Generated Unified Format) ones that support both Mac and Windows, only one test had the N1X (128 GB) beat the M5 Pro (64 GB), and that was Qwen 3.8 Q4. On the heavier Q8 model, the M5 Pro pulls ahead once again, and the gap remains as the loads increase.

The comparison with the M5 Pro MacBook Pro is quite interesting because of the price points. The Surface Laptop Ultra (as tested) is priced at $5,900, while the MacBook Pro (as tested) comes in at $3,700. That’s $2,200 more for worse performance. It should be noted that apps will become more optimized for RTX Spark, and the performance gap will reduce over time. Furthermore, gaming-focused scenarios still favor the RTX Spark, though our testing shows it performs close to the RTX 5060 or RTX 5070 laptop GPUs.

The NVIDIA RTX Spark (featuring the N1X chip) and the Apple M5 Pro represent a major showdown between ARM-based architectures and unified memory systems, both designed for content creators and local artificial intelligence (AI) workloads on laptops.

Key Specifications:

• NVIDIA RTX Spark (N1X):

• CPU: Up to 20 ARM-based cores (available in 18- or 20-core variants, balancing performance and efficiency). 

• GPU: Blackwell architecture with up to 6,144 CUDA cores. 

• Unified Memory / Bandwidth: Utilizes LPDDR5X with a bandwidth of approximately 273 GB/s to 300 GB/s (depending on the model/configuration). 

• Operating System: Runs Windows 11 (utilizing x86 emulation via Prism).

• Apple M5 Pro:

• CPU: Apple’s proprietary ARM-based architecture (available in 14-, 15-, or 18-core configurations). 

• GPU: Apple’s high-efficiency integrated GPU. 

• Unified Memory / Bandwidth: Approximately 307 GB/s of memory bandwidth. 

• Operating System: macOS (with native optimization via MLX and creative software suites).

Performance and Comparison:

• Artificial Intelligence (AI) and Local Models:

• The RTX Spark excels in generative AI workflows (such as image generation, Stable Diffusion, or Flux) thanks to NVIDIA’s CUDA software ecosystem and dedicated accelerators. 

• Conversely, for the inference of smaller language models using optimized frameworks (like MLX on Mac), the M5 Pro holds an advantage in efficiency and raw speed in certain scenarios—though the M5 Max outperforms both, benefiting from significantly higher memory bandwidth. • Content Creation and Video Editing:

• The MacBook Pro with the M5 Pro chip is consistently faster and more stable in applications such as Adobe Photoshop, Lightroom, Premiere Pro, and DaVinci Resolve Studio (all of which feature mature optimization for Apple Silicon chips). 

• The RTX Spark (found in models like the Asus ProArt or Surface Laptop Ultra) still suffers from a lack of optimization in certain editing software, leading to higher latency or dropped frames when handling complex timelines, especially when compared directly to Apple’s hardware.

• Bandwidth:

• The M5 Pro offers approximately 307 GB/s of bandwidth, surpassing the 273 GB/s – 300 GB/s range of the RTX Spark in terms of token decoding speed and memory bottleneck mitigation.

The NVIDIA RTX Spark (an SoC featuring a 20-core ARM CPU and an integrated Blackwell-based GPU equivalent to an RTX 5070) and the Apple M5 Pro (an Apple Silicon architecture with up to 18 cores) represent distinct visions for high-performance portable computing.

Key differences:

• Graphics Performance and Gaming: The RTX Spark holds a significant advantage in dedicated graphics, full ray tracing (DirectX 12 Ultimate), support for DLSS 5 with neural rendering, and compatibility with hundreds of games at launch (running either natively or via emulation within the Windows/Microsoft ecosystem). While the M5 Pro excels in efficiency, it is limited when it comes to demanding AAA games and the DirectX graphics ecosystem.

• Local Artificial Intelligence (AI): The RTX Spark integrates cores with direct support for the CUDA ecosystem and is capable of running large-scale local AI models, whereas the M5 Pro relies on the closed-source optimization of macOS and Apple’s unified architecture.

• CPU and Efficiency: The M5 Pro shines in single-thread performance and seamless hardware-software integration (macOS), consuming less power during everyday productivity tasks. The RTX Spark (utilizing licensed ARM cores, such as the Cortex-X925) focuses on raw GPU power and leverages partnerships with Microsoft to power laptops like the ASUS ProArt and the Microsoft Surface Laptop Ultra.

• Price and Ecosystem: Laptops equipped with the RTX Spark are positioned as high-end, premium devices—often comparable in price to MacBook Pro models—yet they require emulation to run traditional x86 Windows software. In contrast, the M5 Pro operates entirely natively within the macOS environment.

 

mundophone

 

TECH

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Why human learning changes the outcome:

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

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

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

🌐 Why is Human Learning Neglected?

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

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

📊 What Do Humans Need to Learn?

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

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

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

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

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

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

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

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

Max Planck Institute for Human Development