Technology

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.

alternative video upload: https://streamable.com/e/r2uuco

cross-posted from: https://hexbear.net/post/8624879

https://www.axios.com/2026/05/28/ai-spending-roi-enterprise-costs

Archive link https://web.archive.org/web/20260528114303/https://www.axios.com/2026/05/28/ai-spending-roi-enterprise-costs

Why it matters: Companies that rushed to embrace AI are now confronting ballooning IT costs, uncertain productivity gains and growing employee skepticism.

Driving the news: Microsoft canceled most of its Claude Code licenses, in part over costs, according to The Verge, and Uber's COO said AI costs are getting "harder to justify."

An AI consultant tells Axios one of their clients recently spent half a billion dollars in a single month after failing to put usage limits on Claude licenses for employees.

Companies are citing AI's ability to automate jobs as a cause for layoffs, though Anuj Kapur, CEO of CloudBees, told Axios that workforce cuts may simply be "the only lever they can pull" to offset their AI bills.

Consumer sentiment around AI is also nosediving, and employees are rebelling against the use of the technology at work. 

What they're saying: The enterprise is undergoing a "healthy swing" away from AI overuse — or "tokenmaxxing," the push to burn as many AI tokens as possible — Ali Ansari, CEO of model training firm Micro1, told Axios.

Ansari hopes this correction will push companies toward more efficient AI use.
While the market views these tools as working equally well across the enterprise, Ansari says "the reality of AI right now is that it only works for coding."
That disconnect can drive up IT bills without leading to high return on investment in agents, he said. 

Friction point: Corporate AI adoption is running into four unique problems.

Use cases: "Most people default to automating tasks they dislike rather than tasks most valuable to the company," Sophia Velastegui, CEO of Velastegui Ventures and former chief AI officer at Microsoft told Axios. Instead, they should focus on using AI to drive revenue.

Costs: One CTO told Axios that employees were using AI models to check the weather. That gets expensive fast: Enterprise AI plans are not truly 'all you can eat,' and even simple chatbot queries can carry heavy token costs.

cross-posted from: https://lemmy.world/post/47499598

We’ll soon get a chance to see whether, frankly, our last hope, evil corp Google, can still distinguish content created by AI from Human one 🤖

Here’s how I would rank the detection difficulty: 1️⃣ Text 2️⃣ Code 3️⃣ Images 4️⃣ Gifs 5️⃣ Videos If they already fail at level 5, we have a SERIOUS problem.