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Multimodal

It’s make or break time for AI labeling systems

It’s make or break time for AI labeling systems

If robust AI labeling was in place when these swagged out images of Pope Francis went viral, it may have been easier for people to tell they were fake. | Image: via Reddit We're about to find out if the systems designed to make deepfakes and AI-generated content easy to spot are actually up to snuff. SynthID and C2PA Content Credentials, two distinct technologies for invisibly tagging image, video, and audio files with information about their origins, are getting their biggest expansion to date,

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Multimodal

Fine-tune video and image models at scale with NVIDIA NeMo Automodel and 🤗 Diffusers

NVIDIA and Hugging Face have joined forces to bring large-scale fine-tuning of image and video diffusion models into the NeMo Automodel framework, integrated with the Diffusers library. The collaboration aims to make distributed training more accessible for teams working with models that would otherwise be difficult to fine-tune on limited hardware. The result is a more streamlined path from a pretrained diffusion model to a customized one, without requiring deep infrastructure expertise.

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Multimodal

Thinking Machines Lab Releases Inkling: A 975B-Parameter Open-Weights Multimodal MoE With 41B Active Parameters And Controllable Thinking Effort

Thinking Machines Lab has released Inkling, a 975B-parameter open-weights multimodal model built on a Mixture-of-Experts architecture that keeps only 41B parameters active at any given time. Licensed under Apache 2.0, it accepts text, image, and audio inputs and offers a 1M-token context window. Rather than competing for top benchmark rankings, the model is positioned as a customizable base with adjustable reasoning depth.