The ruling holds that a company that designs, trains, operates, and manages an AI system must assume legal liability for any damages caused by the responses it generates.
Concept art from Dear Upstairs Neighbors that used to train custom builds of Google’s Veo and Imagen models. | Image: Google DeepMind
For all the noise that's been made about how generative AI is poised to revolutionize the filmmaking industry, there haven't really been any projects created with the technology that felt like the sort of entertainment people would pay to see. Most AI firms' video models are still only capable of churning out short bursts of visually inconsistent footage. And some of Hollywood's biggest AI partnerships have suddenly evaporated in ways that make it seem like studios might not be able to rely on the new technology coming out of Silicon Valley. For the most part, short-form video slop appears to be the only thing that major production houses ar …
Read the full story at The Verge.
Extremely powerful large language models (LLMs) still operate as though they’re typing on a keyboard, processing workloads in a simple left-to-right fashion. But in locally-run, single-user scenarios, this sequential processing can leave graphics processing units (GPUs) and tensor processing units (TPUs) underutilized.
Google is betting that DiffusionGemma can get around this bottleneck. The new experimental open model generates text “exceptionally fast,” creating entire blocks of text simultaneously through diffusion techniques rather than through token-by-token processing. The company says this technique results in 4x faster inference compared to auto-regressive models that rely on sequential processing.
It can also save users money. Technology analyst Carmi Levy noted that existing pay-per-token monetization models “penalize the use of less than optimally efficient AI solutions.”
But DiffusionGemma “could herald a new generation of task-defined, efficient solutions that can enable e
Extremely powerful large language models (LLMs) still operate as though they’re typing on a keyboard, processing workloads in a simple left-to-right fashion. But in locally-run, single-user scenarios, this sequential processing can leave graphics processing units (GPUs) and tensor processing units (TPUs) underutilized.
Google is betting that DiffusionGemma can get around this bottleneck. The new experimental open model generates text “exceptionally fast,” creating entire blocks of text simultaneously through diffusion techniques rather than through token-by-token processing. The company says this technique results in 4x faster inference compared to auto-regressive models that rely on sequential processing.
It can also save users money. Technology analyst Carmi Levy noted that existing pay-per-token monetization models “penalize the use of less than optimally efficient AI solutions.”
But DiffusionGemma “could herald a new generation of task-defined, efficient solutions that can enable e
We look at Gemini-SQL2, the text-to-SQL capability Google Research announced on June 12, 2026. Powered by Gemini 3.1 Pro, it posted 80.04% execution accuracy on the BIRD single-model leaderboard. We explain what the score measures, how the leaderboard stacks up, and what Google has not yet disclosed. We also cover use cases and a schema-grounded implementation pattern.
The post Google Releases Gemini-SQL2: Gemini 3.1 Pro Text-to-SQL Scores 80.04% on BIRD Single-Model Leaderboard appeared first on MarkTechPost.
The tech giant said a group called "Outsider Enterprise" used AI to scam hundreds of thousands of victims, sending 2.5 million text messages over a span of two weeks.
A school district in eastern Pennsylvania will install a closed-structure artificial intelligence system to handle repetitive operational tasks for the Pupil Services Department, which manages special education programs.
The tech giant said a group called "Outsider Enterprise" used AI to scam hundreds of thousands of victims, sending 2.5 million text messages over a span of two weeks.
