cross-posted from: https://lemmy.ml/post/48326251

The Commission is proposing the European Technological Sovereignty Package, marking a change in Europe's approach to its tech ecosystems.

The Commission is putting forward a multi-pronged, comprehensive strategy to achieve technological sovereignty, with initiatives that are interconnected and mutually reinforcing across each stage of the value chain, from chips, to infrastructure, to software, cloud and AI, and in synergy with past and ongoing initiatives such as AI Factories and AI Gigafactories.

This is reflected in four initiatives:

• The Chips Act 2.0 to strengthen the semiconductor ecosystem and supply chain resilience, and boost domestic demand
• The Cloud and AI Development Act (CADA) to unlock the potential AI and cloud, to transform our industrial ecosystems and improve societal outcomes
• The EU Open Source Strategy to reduce dependencies across the entire technology stack
• A Strategic Roadmap for Digitalisation and AI in Energy

cross-posted from: https://lemmy.ml/post/48326251

The Commission is proposing the European Technological Sovereignty Package, marking a change in Europe's approach to its tech ecosystems.

The Commission is putting forward a multi-pronged, comprehensive strategy to achieve technological sovereignty, with initiatives that are interconnected and mutually reinforcing across each stage of the value chain, from chips, to infrastructure, to software, cloud and AI, and in synergy with past and ongoing initiatives such as AI Factories and AI Gigafactories.

This is reflected in four initiatives:

• The Chips Act 2.0 to strengthen the semiconductor ecosystem and supply chain resilience, and boost domestic demand
• The Cloud and AI Development Act (CADA) to unlock the potential AI and cloud, to transform our industrial ecosystems and improve societal outcomes
• The EU Open Source Strategy to reduce dependencies across the entire technology stack
• A Strategic Roadmap for Digitalisation and AI in Energy

AV2 is the next-generation video coding specification from the Alliance for Open Media (AOMedia). Building on the foundation of AV1, AV2 is engineered to provide superior compression efficiency, enabling high-quality video delivery at significantly lower bitrates. It is optimized for the evolving demands of streaming, broadcasting, and real-time video conferencing.

This specification serves as the definitive technical reference for AV2 implementations. It outlines the bitstream syntax, semantics, and decoding processes required to ensure full conformance.

AV2 provides enhanced support for AR/VR applications, split-screen delivery of multiple programs, improved handling of screen content, and an ability to operate over a wider visual quality range.

cross-posted from: https://lemmy.ml/post/48171005

This is an abridged version of 2026 RISC-V Market Report and Ecosystem Guide by the SHDgroup, provided at no charge thanks to the support of their sponsors. An unabridged version is also available with over 200 pages and comes with a spreadsheet containing over 300 tables of detailed information. In both versions, the intention is to provide a comprehensive examination of the rapidly expanding semiconductor market, including how it is evolving alongside the concurrent emergence of RISC-V and the influence of AI. The accelerating build-out of data centers for AI inferencing and training and Large Language Models (LLMs) is having a profound impact on semiconductor revenues worldwide. This impact extends to the adoption of the RISC-V ISA in an increasing number of SoCs aimed at including some level of AI functionality in the silicon solution. These impacts also extend to the Semiconductor Intellectual Property (SIP) vendors as they look to accommodate the acceleration of the different Neural Networks being used and EDA Tool vendors as they look to infuse AI functionality into their EDA tools to aid the productivity of silicon designers.

The introduction of RISC-V has fueled extensive CPU architectural exploration, visibly impacting device revenues, unit shipments, design starts, business models and IP licensing revenues on a global basis. The pervasive integration of AI across applications is a primary catalyst in today's semiconductor market. The RISC-V architecture has notably influenced SoC designers and architects and is poised to drive a substantial share of designs, revenues, and unit shipments in the coming years.

cross-posted from: https://lemmy.ml/post/48171005

This is an abridged version of 2026 RISC-V Market Report and Ecosystem Guide by the SHDgroup, provided at no charge thanks to the support of their sponsors. An unabridged version is also available with over 200 pages and comes with a spreadsheet containing over 300 tables of detailed information. In both versions, the intention is to provide a comprehensive examination of the rapidly expanding semiconductor market, including how it is evolving alongside the concurrent emergence of RISC-V and the influence of AI. The accelerating build-out of data centers for AI inferencing and training and Large Language Models (LLMs) is having a profound impact on semiconductor revenues worldwide. This impact extends to the adoption of the RISC-V ISA in an increasing number of SoCs aimed at including some level of AI functionality in the silicon solution. These impacts also extend to the Semiconductor Intellectual Property (SIP) vendors as they look to accommodate the acceleration of the different Neural Networks being used and EDA Tool vendors as they look to infuse AI functionality into their EDA tools to aid the productivity of silicon designers.

The introduction of RISC-V has fueled extensive CPU architectural exploration, visibly impacting device revenues, unit shipments, design starts, business models and IP licensing revenues on a global basis. The pervasive integration of AI across applications is a primary catalyst in today's semiconductor market. The RISC-V architecture has notably influenced SoC designers and architects and is poised to drive a substantial share of designs, revenues, and unit shipments in the coming years.

cross-posted from: https://lemmy.ml/post/47499847

BayLibre is proud to announce a successful collaboration with SpacemiT to enable initial functionalities of Android 16 on the SpacemiT K1 (RISC-V RVA22 + RVV 1.0) System-on-Chip (SOC). This achievement marks a significant step toward validating and accelerating Android enablement on high-end RISC-V platforms.

The main objective of this project was to validate the feasibility of porting modern Android to recent, high-performance RISC-V platforms. Furthermore, this work serves as crucial preparation for Android enablement on upcoming RISC-V profile RVA23 SOCs, as much of the effort and code will be directly reusable.

cross-posted from: https://lemmy.ml/post/47263342

The investment will be used to strengthen the structural reliability and security of KDE's core infrastructure, including Plasma, KDE Linux, and the frameworks underlying its communication services.

cross-posted from: https://lemmy.ml/post/47263342

The investment will be used to strengthen the structural reliability and security of KDE's core infrastructure, including Plasma, KDE Linux, and the frameworks underlying its communication services.

The investment will be used to strengthen the structural reliability and security of KDE's core infrastructure, including Plasma, KDE Linux, and the frameworks underlying its communication services.

Current approaches to addressing deceptive design largely focus on visible interface manipulations, commonly referred to as "dark patterns". With the rise of generative AI, deception is becoming more difficult to spot and easier to live with, as it is quietly embedded in default settings, automated suggestions, and conversational interactions rather than discrete interface elements. These subtle, normalised forms of influence, which Simone Natale frames as "banal deception", shape everyday digital use and blur the line between AI-enabled assistance and manipulation.

This position paper explores banality as a lens through which to reason through deception in generative AI experiences, especially with chatbots. We explore what Natale describes as users' own involvement in their deception, and argue that this perspective could lead to future work for introducing friction to safeguard users from deception in generative AI interactions, such as empowering users through raising awareness, providing them with intervention tools, and regulatory or enforcement improvements. We present these concepts as points for discussion for the deceptive design scholarly community.

Full paper: PDF | HTML | TeX source

cross-posted from: https://lemmy.ml/post/47169376

Over the past decade, the AI industry has come to exert an unprecedented economic, political and societal power and influence. It is therefore critical that we comprehend the extent and depth of pervasive and multifaceted capture of AI regulation by corporate actors in order to contend and challenge it. In this paper, we first develop a taxonomy of mechanisms enabling capture to provide a comprehensive understanding of the problem. Grounded in design science research (DSR) methodologies and extensive scoping review of existing literature and media reports, our taxonomy of capture consists of 27 mechanisms across five categories. We then develop an annotation template incorporating our taxonomy, and manually annotate and analyse 100 news articles. The purpose behind this analysis is twofold: validate our taxonomy and provide a novel quantification of capture mechanisms and dominant narratives. Our analysis identifies 249 instances of capture mechanisms, often co-occurring with narratives that rationalise such capture. We find that the most recurring categories of mechanisms are Discourse & Epistemic Influence, concerning narrative framing, and Elusion of law, related to violations and contentious interpretations of antitrust, privacy, copyright and labour laws. We further find that Regulation stifles innovation, Red tape and National Interest are the most frequently invoked narratives used to rationalise capture. We emphasize the extent and breadth of regulatory capture by coalescing forces -- Big AI and governments -- as something policy makers and the public ought to treat as an emergency. Finally, we put forward key lessons learned from other industries along with transferable tactics for uncovering, resisting and challenging Big AI capture as well as in envisioning counter narratives.

Full paper: PDF | HTML | TeX source

cross-posted from: https://lemmy.ml/post/47169376

Over the past decade, the AI industry has come to exert an unprecedented economic, political and societal power and influence. It is therefore critical that we comprehend the extent and depth of pervasive and multifaceted capture of AI regulation by corporate actors in order to contend and challenge it. In this paper, we first develop a taxonomy of mechanisms enabling capture to provide a comprehensive understanding of the problem. Grounded in design science research (DSR) methodologies and extensive scoping review of existing literature and media reports, our taxonomy of capture consists of 27 mechanisms across five categories. We then develop an annotation template incorporating our taxonomy, and manually annotate and analyse 100 news articles. The purpose behind this analysis is twofold: validate our taxonomy and provide a novel quantification of capture mechanisms and dominant narratives. Our analysis identifies 249 instances of capture mechanisms, often co-occurring with narratives that rationalise such capture. We find that the most recurring categories of mechanisms are Discourse & Epistemic Influence, concerning narrative framing, and Elusion of law, related to violations and contentious interpretations of antitrust, privacy, copyright and labour laws. We further find that Regulation stifles innovation, Red tape and National Interest are the most frequently invoked narratives used to rationalise capture. We emphasize the extent and breadth of regulatory capture by coalescing forces -- Big AI and governments -- as something policy makers and the public ought to treat as an emergency. Finally, we put forward key lessons learned from other industries along with transferable tactics for uncovering, resisting and challenging Big AI capture as well as in envisioning counter narratives.

Full paper: PDF | HTML | TeX source