New AI classifier for indicating AI-written text
OpenAI launches a classifier trained to distinguish between AI-written and human-written text.
19 articles
OpenAI launches a classifier trained to distinguish between AI-written and human-written text.
Hugging Face reviews the state of computer vision on its platform: object detection, segmentation, image classification and visual foundation models. Growing integration with transformers and datasets to streamline access and deployment.
Hugging Face releases guide on AI-powered 2D asset generation for game development. Covers diffusion models, fine-grained output control, production pipeline integration. Examples with Stable Diffusion and ControlNet.
Hugging Face presents LoRA (Low-Rank Adaptation) for efficient Stable Diffusion fine-tuning. This method drastically reduces trainable parameters and memory requirements, enabling fine-tuning on standard hardware.
Hugging Face analyzes what makes a dialog agent useful. The article examines required capabilities, evaluation metrics, and practical deployment challenges for conversational agents in production.
Hugging Face announces Optimum integration with ONNX Runtime to accelerate model training. The solution reduces latency and improves throughput through computation graph optimization and automatic quantization.
OpenAI and Microsoft extend their strategic partnership. No financial or technical details provided in the announcement.
Hugging Face explores AI-driven 3D asset generation for game development. The article covers models, techniques, and tools enabling automatic creation of 3D models, textures, and environments from text descriptions or images.
Hugging Face introduces Mask2Former and OneFormer for universal image segmentation. Mask2Former improves performance on semantic, instance, and panoptic segmentation tasks. OneFormer unifies all three tasks in a single trainable model.
PaddlePaddle, Baidu's open-source deep learning framework, joins the Hugging Face Hub. Users can now share PaddlePaddle models, datasets, and spaces on the collaborative platform.
Hugging Face releases a guide for computing image similarity using its Datasets and Transformers libraries. The approach leverages visual embeddings from pre-trained models to compare and rank images.
OpenAI, Georgetown, and Stanford released a report on risks of LLM misuse for disinformation campaigns. Collaborative study (October 2021 workshop, 30 experts) proposing a framework to analyze threats and mitigation measures.
Hugging Face documents building a complete farming game in 5 days using AI. Part 2: vision model integration for crop recognition, procedural level generation, and performance optimization through quantization.
OpenAI uses GPT-3 to rapidly extract nuanced insights from customer feedback, automating large-scale feedback analysis.
OpenAI announces fine-tuning GPT-3 to automate and scale done-for-you video creation. The technique enables generating personalized videos without manual intervention.
Hugging Face introduces a guide to graph machine learning, covering fundamental concepts, graph neural network architectures, and practical applications. The content explores how to process graph-structured data for classification, prediction, and clustering tasks.
Hugging Face documents building a complete farming game in 5 days using AI. Leverages open-source models for asset generation, code, and game design. Demonstrates practical workflow for integrating AI in indie game development.
Intel and Hugging Face optimize PyTorch transformers on Sapphire Rapids processors. First part of a series on hardware acceleration of language models through quantization and CPU optimization techniques.
OpenAI explores using GPT-3 to generate next-generation AI-powered characters, enabling applications in gaming, animation, and interactive experiences.