The art of ordering and culling your possessions before you die should extend to your documents, photos, and digital accounts. Digital generated image of semi transparent multiple data server discs on white background. After Adam Liljenberg's grandmother died, his grandfather was ready to downsize and move into an assisted living facility. As Swedes, they were familiar with Swedish death cleaning, the idea that as you near the end of life, you declutter and organize your belongings so as not to burden those who survive you. When Liljenberg arrived to help his grandfather sort through his possessions, he didn't expect to be rescuing digital photos off a phone full of malware.

As Windows 10 Support Ends, Microsoft Is'Rewriting' Windows 11 Around AI All Windows 11 users will soon be able to talk to the Copilot AI assistant more easily via voice, and Copilot Vision can understand the context of your screen. Microsoft saved its most powerful AI tools for paying customers in the first phase of its AI evolution. Now, the company has announced a series of Copilot features coming to all Windows 11 PCs, including Voice, Copilot Vision, and Copilot Actions. Alongside the update, Microsoft is launching an ad campaign to expose people to these new features. Windows 10 support ended on October 14, and we're about to see a wave of people upgrade to Windows 11; Microsoft seems intent on putting advanced Copilot features at the fingertips of as many people as possible--and convincing them they're worth using.

ACM encourages its members to take a direct hand in shaping the future of the association. There are more ways than ever to get involved.

A large body of research has employed Machine Learning (ML) models to develop learned operating systems (OSes) and kernels. The latter dynamically adapts to the job load and dynamically adjusts resources (CPU, IO, memory, network bandwidth) allocation to respond to the actual user demand. What this work has in common is that it utilizes ML to improve kernel decisions. To this day, and to the best of our knowledge, no work has taken the opposite direction, i.e., using OS to improve ML. While some work proposes applying system-level optimizations to ML algorithms, they do not tailor the OS to adapt to the ML context. To address this limitation, we take an orthogonal approach in this paper by leveraging the OS to enhance the performance of ML models and algorithms. We explore the path towards an ML-specialized OS, MaLV-OS. MaLV-OS rethinks the OS architecture to make it specifically tailored to ML workloads, especially in virtualized clouds, which are now widely used to run ML applications. MaLV-OS envisioned architecture includes (1) a micro-kernel, Micro-LAKE, which allows kernel space applications to use the GPU, and (2) an MLaaS (ML as a Service) subsystem that gathers ML models to help Micro-LAKE with memory management and CPU scheduling. MaLV-OS architecture also offloads system-sensitive parts of the models to the OS, to lighten the model complexity and programming, and speed up its execution. Finally, MaLV-OS integrates an open-source GPU virtualization software, merged directly into the hypervisor. For more flexibility, MaLV-OS vision is to enable the virtual machine to dynamically select MLaaS policies that can improve the performance of the model the user is running. Because MLaaS is designed as loadable kernel modules, the MaLV-OS architecture enables the dynamic addition of new capabilities to the MLaaS subsystem.

As intelligent systems permeate edge devices, cloud infrastructure, and embedded real-time environments, this research proposes a new OS kernel architecture for intelligent systems, transforming kernels from static resource managers to adaptive, AI-integrated platforms. Key contributions include: (1) treating Loadable Kernel Modules (LKMs) as AI-oriented computation units for fast sensory and cognitive processing in kernel space; (2) expanding the Linux kernel into an AI-native environment with built-in deep learning inference, floating-point acceleration, and real-time adaptive scheduling for efficient ML workloads; and (3) introducing a Neurosymbolic kernel design leveraging Category Theory and Homotopy Type Theory to unify symbolic reasoning and differentiable logic within OS internals. Together, these approaches enable operating systems to proactively anticipate and adapt to the cognitive needs of autonomous intelligent applications.

The surging demand for GPUs in datacenters for machine learning (ML) has made efficient GPU utilization crucial. However, meeting the diverse needs of ML models while optimizing resource usage is challenging. To enable transparent, fine-grained GPU management that maximizes utilization and energy efficiency while maintaining strong isolation, an operating system (OS) approach is needed. This paper introduces LithOS, a first step toward a GPU OS. LithOS includes the following new abstractions and mechanisms for efficient GPU resource management: (i) a novel TPC Scheduler that supports spatial scheduling at the granularity of individual TPCs, unlocking efficient TPC stealing between workloads; (ii) transparent kernel atomization to reduce head-of-line blocking and enable dynamic resource reallocation mid-execution; (iii) a lightweight hardware right-sizing mechanism that determines the minimal TPC resources needed per atom; and (iv) a transparent power management mechanism that reduces power consumption based on in-flight work behavior. We implement LithOS in Rust and evaluate its performance across extensive ML environments, comparing it to state-of-the-art solutions from NVIDIA and prior research. For inference stacking, LithOS reduces tail latencies by 13x compared to MPS; compared to the best SotA, it reduces tail latencies by 3x while improving aggregate throughput by 1.6x. In hybrid inference-training stacking, LithOS reduces tail latencies by 4.7x compared to MPS; compared to the best SotA, it reduces tail latencies 1.18x while improving aggregate throughput by 1.35x. Finally, for a modest performance hit under 4%, LithOS's right-sizing provides a quarter of GPU capacity savings on average, while for a 7% hit, its power management yields a quarter of a GPU's energy savings. Overall, LithOS increases GPU efficiency, establishing a foundation for future OS research on GPUs.

Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. I was 21 when I first watched Spike Jonze's 2013 sci-fi romance Her in theaters in New York City--a then–fresh college graduate teeming with the potent and deluded optimism that came with being a very broke and online millennial hoping to change the world. Her sparked some of my first reflections about whether tech innovation is inherently good or bad for society, and helped validate my early moral quandaries and panic at the time. I was graduating at the first turn of a recovering recession (mainly due to big tech investments in digital and social media) and securing my first full-time role as an online reporter. Though I was eager and rosy, a quiet, worried voice also began growing inside of me. Me, my job, my realities, were entirely dependent on tech--mainly Facebook content dissemination and programmatic turnkey digital ads--and I was not sure these huge tech investments by our broligarchical founding fathers would lead us anywhere good.

Nearly every TV on the market today is a smart TV, but not every operating system is a winner. A media streaming device lets you pair whichever user interface you prefer with just about any screen that has an HDMI port. In some cases, such as with older or less expensive smart TVs, a streaming stick or dongle could even be speedier and less glitchy than your TV's built-in system. At home, these handy gadgets make it easier for cord cutters to watch the millions of hours of content streaming services provide without cable. And while traveling, a streaming player lets you watch your preferred content on hotel sets (without painstakingly typing in a bunch of passwords or activation codes). We tested out streaming players from Roku, Google, Apple, Amazon and more, gauging the usability and the performance of each to come up with our list of the best streaming devices you can buy. Google's TV Streamer, the Apple TV 4K, Amazon's Fire TV Sticks and Roku devices are the most popular players in the space.

The underlying framework for controlling autonomous robots and complex automation applications are Operating Systems (OS) capable of scheduling perception-and-control tasks, as well as providing real-time data communication to other robotic peers and remote cloud computers. In this paper, we introduce CyberCortex.AI, a robotics OS designed to enable heterogeneous AI-based robotics and complex automation applications. CyberCortex.AI is a decentralized distributed OS which enables robots to talk to each other, as well as to High Performance Computers (HPC) in the cloud. Sensory and control data from the robots is streamed towards HPC systems with the purpose of training AI algorithms, which are afterwards deployed on the robots. Each functionality of a robot (e.g. sensory data acquisition, path planning, motion control, etc.) is executed within a so-called DataBlock of Filters shared through the internet, where each filter is computed either locally on the robot itself, or remotely on a different robotic system. The data is stored and accessed via a so-called \textit{Temporal Addressable Memory} (TAM), which acts as a gateway between each filter's input and output. CyberCortex.AI has two main components: i) the CyberCortex.AI.inference system, which is a real-time implementation of the DataBlock running on the robots' embedded hardware, and ii) the CyberCortex.AI.dojo, which runs on an HPC computer in the cloud, and it is used to design, train and deploy AI algorithms. We present a quantitative and qualitative performance analysis of the proposed approach using two collaborative robotics applications: \textit{i}) a forest fires prevention system based on an Unitree A1 legged robot and an Anafi Parrot 4K drone, as well as \textit{ii}) an autonomous driving system which uses CyberCortex.AI for collaborative perception and motion control.

Software systems are inherently complex. In addition, any ML model is, at its core, probabilistic in nature and hence, suffers from the challenge of uncertainty [2, 3, 4]. The complexity of a software system combined with the non-deterministic nature of an ML model can introduce variability - the phenomenon where a piece of software behaves differently when the development or the runtime environment changes although the internal software artifacts such as code, and input data are exactly the same. In practice it is very likely that development and deployment environments are different, hence, understanding how an ML model may behave differently after deployment compared to how it behaved in the development environment is a crucial aspect of AI-based software development. For example, an arbitrary face recognition system achieving an F1-score of, say 0.9, in the development environment does not guarantee that it will on average achieve a similar F1-score once deployed in a different environment configuration.