Capabilities of GPT-4 on Medical Challenge Problems
GPT-4, without any specialized prompt crafting, exceeds the passing score on USMLE by over 20 points and outperforms domain-expert models like Med-PaLM.
GPT-4 can be used in medical education, assessment, and clinical practice. It has potential to improve accuracy and safety.
The Quantization Model of Neural Scaling
The model explains both the observed power law dropoff of loss with model and data size, and also the sudden emergence of new capabilities with scale.
The model can help explain the behavior of large neural networks and may lead to improvements in scaling strategies for AI models.
Ablating Concepts in Text-to-Image Diffusion Models
The paper proposes an efficient method of ablating concepts in a pretrained model to prevent the generation of copyrighted or unwanted images.
The proposed method can help prevent the generation of copyrighted or unwanted images without retraining the model from scratch.
CoBIT: A Contrastive Bi-directional Image-Text Generation Model
Presents a model that unifies three pre-training objectives (contrastive, image-to-text, and text-to-image) and achieves superior performance in various image and text tasks.
Can improve image understanding, image-text understanding, and text-based content creation in business operations, particularly in zero-shot scenarios.
DreamBooth3D: Subject-Driven Text-to-3D Generation
Presents an approach to personalize text-to-3D generative models from a few captured images of a subject.
Can help businesses create high-quality, subject-specific 3D assets with text-driven modifications for various applications such as virtual product prototyping and marketing.