The second major cloud outage in less than two weeks, Azure's downtime highlights the "brittleness" of a digital ecosystem that depends on a few companies never making mistakes. Microsoft's Azure cloud platform, its widely used 365 services, Xbox, and Minecraft started suffering outages at roughly noon Eastern time on Wednesday, the result of what Microsoft said was "an inadvertent configuration change." The incident--which marks the second major cloud provider outage in less than two weeks--highlights the instability of an internet built largely on infrastructure run by a few tech giants. Microsoft's problems specifically originated from Azure's Front Door content delivery network and emerged just hours before Microsoft's scheduled earnings announcement. The company website, including its investor relations page, was still down on Wednesday afternoon, and the Azure status page where Microsoft provides updates was having intermittent issues as well.

Experts say outages like the one that Amazon experienced this week are almost inevitable given the complexity and scale of cloud technology--but the duration serves as a warning. A sprawling Amazon Web Services cloud outage that began early Monday morning illustrated the fragile interdependencies of the internet as major communication, financial, health care, education, and government platforms around the world suffered disruptions. As the day wore on, AWS diagnosed and began working to correct the issue, which stemmed from the company's critical US-EAST-1 region based in northern Virginia. But the cascade of impacts took time to fully resolve. Researchers reflecting on the incident particularly highlighted the length of Monday's outage, which started around 3 am ET on Monday, October 20.

A decision tree is an easy-to-understand tool that has been widely used for classification tasks. On the one hand, due to privacy concerns, there has been an urgent need to create privacy-preserving classifiers that conceal the user's input from the classifier. On the other hand, with the rise of cloud computing, data owners are keen to reduce risk by outsourcing their model, but want security guarantees that third parties cannot steal their decision tree model. To address these issues, Joye and Salehi introduced a theoretical protocol that efficiently evaluates decision trees while maintaining privacy by leveraging their comparison protocol that is resistant to timing attacks. However, their approach was not only inefficient but also prone to side-channel attacks. Therefore, in this paper, we propose a new decision tree inference protocol in which the model is shared and evaluated among multiple entities. We partition our decision tree model by each level to be stored in a new entity we refer to as a "level-site." Utilizing this approach, we were able to gain improved average run time for classifier evaluation for a non-complete tree, while also having strong mitigations against side-channel attacks.

This year, U.S. rail carrier Amtrak will be installing two novel inspection gateways from Duos Technologies along its busy Northeast Corridor. The barn-like Duos structures straddle railway tracks; as passenger trains speed through at up to 125 miles per hour, 97 cameras and dozens of LED lights arrayed around the sides, top, and bottom of the tracks will capture thousands of high-resolution images of the railcars. These images are aggregated and processed on site in real time to present a complete, 360-degree, highly detailed view of the train. Artificial intelligence (AI) algorithms running on Nvidia GPUs will analyze the images locally; if the model flags a potential structural or mechanical flaw, train personnel will be notified in less than a minute. The Duos portal is one of many new examples of what is loosely categorized as edge AI, or the deployment and operation of AI models outside of massive cloud datacenters.

It is common practice to outsource the training of machine learning models to cloud providers. Clients who do so gain from the cloud's economies of scale, but implicitly assume trust: the server should not deviate from the client's training procedure. A malicious server may, for instance, seek to insert backdoors in the model. Detecting a backdoored model without prior knowledge of both the backdoor attack and its accompanying trigger remains a challenging problem. In this paper, we show that a client with access to multiple cloud providers can replicate a subset of training steps across multiple servers to detect deviation from the training procedure in a similar manner to differential testing. Assuming some cloud-provided servers are benign, we identify malicious servers by the substantial difference between model updates required for backdooring and those resulting from clean training. Perhaps the strongest advantage of our approach is its suitability to clients that have limited-to-no local compute capability to perform training; we leverage the existence of multiple cloud providers to identify malicious updates without expensive human labeling or heavy computation. We demonstrate the capabilities of our approach on an outsourced supervised learning task where $50\%$ of the cloud providers insert their own backdoor; our approach is able to correctly identify $99.6\%$ of them. In essence, our approach is successful because it replaces the signature-based paradigm taken by existing approaches with an anomaly-based detection paradigm. Furthermore, our approach is robust to several attacks from adaptive adversaries utilizing knowledge of our detection scheme.

The deployment of Machine Learning models at cloud have grown by tech companies. Hardware requirements are higher when these models involve Deep Learning (DL) techniques and the cloud providers' costs may be a barrier. We explore deploying DL models using for experiments the GECToR model, a DL solution for Grammatical Error Correction, across three of the major cloud platforms (AWS, Google Cloud, Azure). We evaluate real-time latency, hardware usage and cost at each cloud provider by 7 execution environments with 10 experiments reproduced. We found that while GPUs excel in performance, they had an average cost 300% higher than solutions without GPU. Our analysis also identifies that processor cache size is crucial for cost-effective CPU deployments, enabling over 50% of cost reduction compared to GPUs. This study demonstrates the feasibility and affordability of cloud-based DL inference solutions without GPUs, benefiting resource-constrained users like startups.

As the use of facial recognition technology is expanding in different domains, ensuring its responsible use is gaining more importance. This paper conducts a comprehensive literature review of existing studies on facial recognition technology from the perspective of privacy, which is one of the key Responsible AI principles. Cloud providers, such as Microsoft, AWS, and Google, are at the forefront of delivering facial-related technology services, but their approaches to responsible use of these technologies vary significantly. This paper compares how these cloud giants implement the privacy principle into their facial recognition and detection services. By analysing their approaches, it identifies both common practices and notable differences. The results of this research will be valuable for developers and businesses by providing them insights into best practices of three major companies for integration responsible AI, particularly privacy, into their cloud-based facial recognition technologies.

The Israeli military's reliance on Microsoft's cloud technology and artificial intelligence systems surged during the most intensive phase of its bombardment of Gaza, leaked documents reveal. The files offer an inside view of how Microsoft deepened its relationship with Israel's defence establishment after 7 October 2023, supplying the military with greater computing and storage services and striking at least 10m in deals to provide thousands of hours of technical support. Microsoft's deep ties with Israel's military are revealed in an investigation by the Guardian with the Israeli-Palestinian publication 972 Magazine and a Hebrew-language outlet, Local Call. It is based in part on documents obtained by Drop Site News, which has published its own story. The investigation, which also draws on interviews with sources from across Israel's defence and intelligence establishment, sheds new light on how the Israel Defense Forces (IDF) turned to major US tech companies to meet the technological demands of war. After launching its offensive in Gaza in October 2023, the IDF faced a sudden rush in demand for storage and computing power, leading it to swiftly expand its computing infrastructure and embrace what one commander described as "the wonderful world of cloud providers".

Generative AI is transforming enterprise application development by enabling machines to create content, code, and designs. These models, however, demand substantial computational power and data management. Cloud computing addresses these needs by offering infrastructure to train, deploy, and scale generative AI models. This review examines cloud services for generative AI, focusing on key providers like Amazon Web Services (AWS), Microsoft Azure, Google Cloud, IBM Cloud, Oracle Cloud, and Alibaba Cloud. It compares their strengths, weaknesses, and impact on enterprise growth. We explore the role of high-performance computing (HPC), serverless architectures, edge computing, and storage in supporting generative AI. We also highlight the significance of data management, networking, and AI-specific tools in building and deploying these models. Additionally, the review addresses security concerns, including data privacy, compliance, and AI model protection. It assesses the performance and cost efficiency of various cloud providers and presents case studies from healthcare, finance, and entertainment. We conclude by discussing challenges and future directions, such as technical hurdles, vendor lock-in, sustainability, and regulatory issues. Put together, this work can serve as a guide for practitioners and researchers looking to adopt cloud-based generative AI solutions, serving as a valuable guide to navigating the intricacies of this evolving field.

All products featured on WIRED are independently selected by our editors. However, we may receive compensation from retailers and/or from purchases of products through these links. I wait to meet Xavier Niel in a room that feels fitting for one of France's richest men. Niel is the original French internet mogul, of the generation before founders wore T-shirts to the office. His team wears suits; he arrives in a classic white shirt.