Tech
Interrupting encoder training in diffusion models enables more efficient generative AI
A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving generative AI models. The method reinterpreted Schrödinger bridge models as variational autoencoders with infinitely many latent variables, reducing computational costs and preventing overfitting. By appropriately interrupting the training of the encoder, this approach enabled development of more efficient generative AI, with broad applicability beyond standard diffusion models.
Diffusion models are among the most widely used approaches in generative AI for creating images and audio. These models generate new data by gradually adding noise (noising) to real samples and then learning how to reverse that process (denoising) back into realistic data. A widely used version, the score-based model, achieves this by the diffusion process connecting the prior to the data with a sufficiently long-time interval. This method, however, has a limitation that when the data differs strongly from the prior, the time intervals of the noising and denoising processes become longer, which causes slowing down sample generation.
Now, a research team from Institute of Science Tokyo (Science Tokyo), Japan, has proposed a new framework for diffusion models that is faster and computationally less demanding. They achieved this by reinterpreting Schrödinger bridge (SB) models, a type of diffusion model, as variational autoencoders (VAEs).
The study was led by graduate student Mr. Kentaro Kaba and Professor Masayuki Ohzeki from the Department of Physics at Science Tokyo, in collaboration with Mr. Reo Shimizu (then a graduate student) and Associate Professor Yuki Sugiyama from the Graduate School of Information Sciences at Tohoku University, Japan. Their findings were published in the Physical Review Research on September 3, 2025.
SB models offer greater flexibility than standard score-based models because they can connect any two probability distributions over a finite time using a stochastic differential equation (SDE). This supports more complex noising processes and higher-quality sample generation. The trade-off, however, is that SB models are mathematically complex and expensive to train.
The proposed method addresses this by reformulating SB models as VAEs with multiple latent variables. “The key insight lies in extending the number of latent variables from one to infinity, leveraging the data-processing inequality. This perspective enables us to interpret SB-type models within the framework of VAEs,” says Kaba.
In this setup, the encoder represents the forward process that maps real data onto a noisy latent space, while the decoder reverses the process to reconstruct realistic samples, and both processes are modeled as SDEs learned by neural networks.
The model employs a training objective with two components. The first is the prior loss, which ensures that the encoder correctly maps the data distribution to the prior distribution. The second is drift matching, which trains the decoder to mimic the dynamics of the reverse encoder process. Moreover, once the prior loss stabilizes, encoder training can be stopped early. This allows us to complete learning faster, reducing the risk of overfitting and preserving high accuracy in SB models.
“The objective function is composed of the prior loss and drift matching parts, which characterizes the training of neural networks in the encoder and the decoder, respectively. Together, they reduce the computational cost of training SB-type models. It was demonstrated that interrupting the training of the encoder mitigated the challenge of overfitting,” explains Ohzeki.
This approach is flexible and can be applied to other probabilistic rule sets, even non-Markov processes, making it a broadly applicable training scheme.
More information:
Kentaro Kaba et al, Schrödinger bridge-type diffusion models as an extension of variational autoencoders, Physical Review Research (2025). DOI: 10.1103/dxp7-4hby
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Interrupting encoder training in diffusion models enables more efficient generative AI (2025, September 29)
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Tech
Men Are Buying Hacking Tools to Use Against Their Wives and Friends
Thousands of men are members of Telegram groups and channels that advertise and sell hacking and surveillance services that can be used to harass friends, wives and girlfriends, and former partners, new research has uncovered. The findings, from a European nonprofit group, also say that the communities are involved in extensive trading, selling, and promotion of a huge variety of abusive content, including nonconsensual intimate images of women, so-called nudifying services, plus folders of images that sellers claim include child sexual abuse material and depictions of incest and rape.
Over six weeks earlier this year, researchers at the algorithmic auditing group AI Forensics analyzed nearly 2.8 million messages sent across 16 Italian and Spanish Telegram communities that are regularly posting abusive content targeting women and girls. More than 24,000 members of the Telegram groups and channels took part in posting 82,723 images, videos, and audio files over the course of the study, the analysis says. Many posts target celebrities and influencers, but men in the groups also frequently victimize women they know.
“We tend to forget that most victims are ordinary women who sometimes don’t even know that their pictures are shared or manipulated in these types of channels,” says Silvia Semenzin, a researcher at AI Forensics who previously exposed Italian Telegram channels engaging in similar behavior as far back as 2019. “The majority of this violence is directed towards people who the perpetrators know,” she says, suggesting that Telegram, which has over 1 billion monthly active users, according to company founder Pavel Durov, should be subject to stricter regulation and classed as a “very large online platform” under Europe’s online safety rules.
The findings come as Durov is fighting back against Russia’s efforts to block the messaging app in that country, which has long positioned itself as a messaging app that allows free speech but has simultaneously been used by some to share terrorist, sexual abuse, and cybercrime materials. Durov is under criminal investigation in France relating to alleged criminal activity taking place on Telegram, although he has consistently denied the allegations.
A Telegram spokesperson tells WIRED that the company removes “millions” of pieces of content per day using “custom AI tools” and has policies in Europe that do not allow the promotion of violence, illegal sexual content including nonconsensual imagery, and other content such as doxing and selling illegal goods and services.
Among the extensive types of abusive content and services observed by the AI Forensics researchers were frequent references to the access, publishing, and doxing of women’s private information, sharing their Instagram or TikTok content, as well as references to spying or hacking. “Victims are often named, tagged, and locatable via shared profile links,” the group’s report says.
One translated post on Telegram titled “Professional hacking on commission” claimed to be able to give customers “access to phone gallery and extraction of photos and videos,” as well as “anonymous social media hacking.” Another message says: “I hack and recover any type of social media service. I can spy on your partner’s account. Send me a private message.”
Across the dataset there were more than 18,000 references to spying or spy content. One post reads: “Hi, do you have the desire to spy on a girl’s gallery? We sell a bot that does it for info DM.” Meanwhile, users were observed asking if people could find phone numbers connected to Instagram accounts and other requests, “who exchanges spy photos and videos?”
Tech
Cisco: Network readiness a determining factor for AI success | Computer Weekly
Research from Cisco has found that as many as two-thirds of industrial organisations have moved to active artificial intelligence (AI) deployments in live operational environments, yet while adoption momentum is strong, infrastructure and organisational alignment – especially networking and security – will dictate who achieves real transformation.
The latest version of the State of industrial AI report 2026 looked to provide a data‑driven view into how industrial organisations are adopting AI, the challenges they face as AI moves into live operations, and the opportunities created as AI becomes embedded in physical systems, infrastructure and workflows.
The study is based on data from a global survey of more than 1,000 operational technology decision‑makers, conducted by Cisco in association with Sapio Research. Respondents were from 19 countries and across 21 industry sectors, representing a range of industries including manufacturing, transportation, logistics, energy and utilities, and more. The study aggregated findings from decision-makers at companies with annual revenues of more than $100m.
Among the top findings were that AI organisations are harnessing AI to drive progress and overcome industry challenges, and that it is now delivering measurable operational benefits, in particular in use cases such as process automation, automated quality inspection, predictive maintenance, logistics and energy forecasting. Strong expected benefits from AI included productivity (59%), cost reduction (42%) and sustainability.
Industrial AI was seen to have moved from a future consideration to active deployment, with 61% of organisations now using AI in live industrial operations where performance, reliability and security have direct physical consequences, and 20% reporting scaled, mature deployments. Across manufacturing, transportation and utilities, AI was found to be powering machine vision, mobility, robotics and safety‑critical operations.
Most organisations indicated that they planned to increase AI spending (83%), and nearly nine in 10 expect meaningful outcomes in the next two years (87%). Yet just as adoption was accelerating, many firms were struggling to sustain and expand deployments, with readiness across network infrastructure, security and skills increasingly determining whether AI can scale consistently across core physical environments.
Indeed, network readiness and security posture were cited as the primary factors shaping how quickly and safely organisations scale AI across connected assets, machines and sites. The report observed that as AI becomes embedded in machines, sensors, vision systems and autonomous operations, organisations face rising demands for reliable connectivity, wireless mobility, predictable latency, edge compute and power, which were making network readiness a gating factor for physical AI deployments.
Just over half of firms (51%) expect significant increases in connectivity and reliability requirements in their industrial networks, and almost all firms (96%) noted that reliable wireless networks are vital for AI. In addition, 97% expected AI workloads to impact their industrial network requirements.
Yet while legacy infrastructure and skills gaps remain secondary challenges, Cisco also cautioned that the study also revealed many organisations were increasingly constrained by readiness gaps in networking infrastructure, cyber security and IT/OT operating models as AI shifts into real‑time, production‑grade use in physical environments.
Another key discovery was that organisations with closer collaboration between IT and operational teams report greater confidence in expanding AI, more stable networks supporting physical operations, and a stronger emphasis on cyber security as a baseline requirement, underscoring the need to build the skills required for scalable AI adoption.
Nearly two in three firms (57%) reported some level of IT/OT collaboration, while 43% reported limited or no collaboration. Just under half (47%) of organisations with limited IT/OT collaboration cited network instability as a top operational challenge to scale AI.
Cyber security was highlighted as shaping both the pace and confidence of AI adoption. Cisco also found that as AI expands connectivity and data flows across industrial environments, security remained the top barrier to scale. At the same time, organisations increasingly view AI as part of the solution, with a majority expecting AI to strengthen monitoring, detection and operational resilience.
“Industrial AI is moving from experimentation into production, where AI systems sense, reason and act in the real world,” said Vikas Butaney, senior vice-president and general manager of secure routing and industrial internet of things at Cisco. “At this stage, success is no longer determined by models alone, but by whether networks, security and teams are ready to support AI at the edge, in motion, and at scale. The research shows that organisations confident in scaling AI are those treating infrastructure, cyber security and IT/OT collaboration as foundational, not optional.”
Tech
The iPhone Gets a D– for Repairability
The iPhone is the least fixable phone on the market, according to repairability experts. Phones from Samsung and Google are not far behind.
The latest repairability ratings are from an annual report called “Failing the Fix” put out today by the consumer advocacy group US PIRG. A 2021 French law required products to be labeled with repairability scores, and US PIRG says this is the first report since then that really shows which companies are—or are not—making progress. The answer is that repairability is progressing much more quickly in some places than others.
The results were good for phones made by Motorola, which got a B+. Google’s phones got a C–. The verdict was worse for Samsung phones, which got a D. Last on the list was Apple with a D–. Apple and Samsung did not immediately respond to requests for comment.
Scores were better for laptops than smartphones, with Asus at the top with a B+ and Apple on the bottom with its MacBooks at a C–.
The authors of the report are hoping that publishing these low scores will encourage manufacturers to do better.
“Putting these right incentives in place could push these companies to make innovations that are actually beneficial,” says Nathan Proctor, senior director of the US PIRG campaign for the right to repair. “Instead of coming up with new ways to jam AI down our throats, you can make stuff that lasts and that we can fix.”
Despite many right-to-repair concessions companies have made—like making their tools, parts, and repair instructions publicly available—those rankings are lower than in years past, largely because of the new information that has been gleaned from European laws requiring repair scores to be printed on product packaging.
The French law grades products based on how easily they can be disassembled, whether documentation and tools are provided, and the availability and price of spare parts. In 2023, the European Union passed a law establishing the European Product Registry for Energy Labelling, a process that grades devices on key repairability factors like whether products have easy access and disassembly, battery endurance, ingress protection like waterproofing, and the durability to handle repeated falls. The rankings go from A to F.
To arrive at its own ratings, US PIRG collates the EPREL and France’s repair indexes with other US-specific factors, like whether companies are actively lobbying against the right to repair or are members of trade associations that do so.
“If you’re buying your equipment from a company that’s spending their money to lobby against your right to repair that thing, that doesn’t speak well for their support, for your ability to fix that,” Proctor says. “So we also dock points for some of those legislative activities.”
Apple’s phones are getting better scores than in years past, like when iPhones were assigned an F rating in 2022. (iPhones got a C– in 2025.) The low rating for Apple’s phones comes down to software support, and how the EU laws track the information about what companies enable in their products. Based on the EU laws, companies have to self-report how their devices meet repair requirements. And those rankings tend to score pretty low.
“When we’ve been grading on a curve, Apple has not been a standout in the bad column,” Proctor says. “But why are we grading on a curve? We should just have longer-lasting products.”
The ultimate goal of these rankings, Proctor says, is to bring attention to the importance of repairability, accessibility, and waste reduction.
“This is an emerging, vitally important issue that we need better leadership on from companies and from other public policy officials,” Proctor says. “We should not be trashing all of our internet-connected stuff every couple of years because it’s impossible to use it with the software. It’s totally unsustainable. It’s crazy. Let’s not build that world. That world is a dystopia.”
“I’m actually pretty confident that some of that stuff’s going to get addressed,” Proctor adds. “Apple engineers are good at making stuff. They’re good at solving problems.”
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