Existing benchmarks suggest voice AI is nearing human-level performance but real-world conversations tell a different story. Voice is rapidly becoming AI’s primary interface. From customer support and healthcare to education,…
What’s next This launch builds on our history of providing context about images in Google Search and exploring new research innovations like Backstory from Google DeepMind. Looking ahead, we will…
Home to leading manufacturers, robotics pioneers and infrastructure builders, Japan is one of the world’s centers of AI — building across the full stack with NVIDIA technologies. NVIDIA and its…
Google released LiteRT.js, a JavaScript binding of LiteRT. LiteRT is Google’s on-device inference library, previously called TensorFlow Lite. LiteRT.js runs .tflite models directly inside the browser. Because inference stays local,…
Heavy computation is a well-known problem in various ML algorithms today, especially when generative AI is applied to text, images, and other unstructured data. One of the principal approaches to…
HuggingFace Transformers has become the foundation of the open-source AI ecosystem, and the recent Transformers v5 release strengthened it with first-class support for Mixture-of-Experts (MoE) models, now the dominant architecture…
What customers are saying Google Cloud customers are already using Gemini’s native audio capabilities to drive real business results, from mortgage processing to customer calls. “Users often forget they’re talking…
Editor’s note: This post is part of the Nemotron Labs blog series, which explores how the latest open models, datasets and training techniques help businesses build specialized AI systems and…
This essay first appeared in Reboot. Credulous, breathless coverage of “AI existential risk” (abbreviated “x-risk”) has reached the mainstream. Who could have foreseen that the smallcaps onomatopoeia “ꜰᴏᴏᴍ” — both…
” style=”width:100%;border:0;height:600px;display:block;overflow:hidden;” scrolling=”no” loading=”lazy” title=”Bonsai 27B interactive explainer”> That architecture shapes the compression method below. How the Compression Works Each weight is a code, with one shared FP16 scale per…
